Augmented gamma belief network operation

ABSTRACT

A method, system and computer readable medium for generating a cognitive insight comprising: receiving data, the data comprising a plurality of examples, each of the plurality of examples comprising an input object and a desired output value, at least some of the plurality of examples being based upon feedback from a user; performing a machine learning operation on the data, the machine learning operation comprising performing an augmented gamma belief network operation, the augmented gamma belief network operation producing an inferred function based upon the data; and, generating a cognitive insight based upon the cognitive profile generated using the inferred function generated by the augmented gamma belief network operation.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to the field of computers andsimilar technologies, and in particular to software utilized in thisfield. Still more particularly, it relates to a method, system andcomputer-usable medium for generating and using cognitive insights via acognitive machine learning architecture.

Description of the Related Art

In general, “big data” refers to a collection of datasets so large andcomplex that they become difficult to process using typical databasemanagement tools and traditional data processing approaches. Thesedatasets can originate from a wide variety of sources, includingcomputer systems, mobile devices, credit card transactions, televisionbroadcasts, and medical equipment, as well as infrastructures associatedwith cities, sensor-equipped buildings and factories, and transportationsystems. Challenges commonly associated with big data, which may be acombination of structured, unstructured, and semi-structured data,include its capture, curation, storage, search, sharing, analysis andvisualization. In combination, these challenges make it difficult toefficiently process large quantities of data within tolerable timeintervals.

Nonetheless, big data analytics hold the promise of extracting insightsby uncovering difficult-to-discover patterns and connections, as well asproviding assistance in making complex decisions by analyzing differentand potentially conflicting options. As such, individuals andorganizations alike can be provided new opportunities to innovate,compete, and capture value.

One aspect of big data is “dark data,” which generally refers to datathat is either not collected, neglected, or underutilized. Examples ofdata that is not currently being collected includes location data priorto the emergence of companies such as Foursquare or social data prior tothe advent companies such as Facebook. An example of data that is beingcollected, but is difficult to access at the right time and place,includes data associated with the side effects of certain spider biteswhile on a camping trip. As another example, data that is collected andavailable, but has not yet been productized of fully utilized, mayinclude disease insights from population-wide healthcare records andsocial media feeds. As a result, a case can be made that dark data mayin fact be of higher value than big data in general, especially as itcan likely provide actionable insights when it is combined withreadily-available data.

SUMMARY OF THE INVENTION

In one embodiment, the invention relates to a method for generating acognitive insight comprising: receiving data, the data comprising aplurality of examples, each of the plurality of examples comprising aninput object and a desired output value, at least some of the pluralityof examples being based upon feedback from a user; performing a machinelearning operation on the data, the machine learning operationcomprising performing an augmented gamma belief network operation, theaugmented gamma belief network operation producing an inferred functionbased upon the data; and, generating a cognitive insight based upon thecognitive profile generated using the inferred function generated by theaugmented gamma belief network operation.

In one embodiment, the invention relates to a system comprising: aprocessor; a data bus coupled to the processor: and a non-transitory,computer-readable storage medium embodying computer program code, thenon-transitory, computer-readable storage medium being coupled to thedata bus. The computer program code interacting with a plurality ofcomputer operations and comprising instructions executable by theprocessor and configured for: receiving data, the data comprising aplurality of examples, each of the plurality of examples comprising aninput object and a desired output value, at least some of the pluralityof examples being based upon feedback from a user; performing a machinelearning operation on the data, the machine learning operationcomprising performing an augmented gamma belief network operation, theaugmented gamma belief network operation producing an inferred functionbased upon the data; and, generating a cognitive insight based upon thecognitive profile generated using the inferred function generated by theaugmented gamma belief network operation.

In another embodiment, the invention relates to a non-transitory,computer-readable storage medium embodying computer program code, thecomputer program code comprising computer executable instructionsconfigured for: receiving data, the data comprising a plurality ofexamples, each of the plurality of examples comprising an input objectand a desired output value, at least some of the plurality of examplesbeing based upon feedback from a user; performing a machine learningoperation on the data, the machine learning operation comprisingperforming an augmented gamma belief network operation, the augmentedgamma belief network operation producing an inferred function based uponthe data; and, generating a cognitive insight based upon the cognitiveprofile generated using the inferred function generated by the augmentedgamma belief network operation.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 depicts an exemplary client computer in which the presentinvention may be implemented;

FIG. 2 is a simplified block diagram of a cognitive inference andlearning system (CILS);

FIG. 3 is a simplified block diagram of a CILS reference modelimplemented in accordance with an embodiment of the invention;

FIG. 4 is a simplified process diagram of CILS operations;

FIG. 5 depicts the lifecycle of CILS agents implemented to perform CILSoperations;

FIG. 6 depicts a cognitive learning framework;

FIGS. 7 a and 7 b are a simplified block diagram of a CILS used tomanage the performance of cognitive learning operations throughout theirlifecycle;

FIG. 8 is a simplified process flow diagram of the performance ofcognitive machine learning operations for generating a hierarchicalabstraction of topics within a corpus;

FIG. 9 shows the use of Gibbs sampling by a cognitive machine learningalgorithm;

FIG. 10 depicts upward-downward sampling operations performed by acognitive machine learning algorithm;

FIG. 11 is a simplified block diagram of the generation of ahierarchical abstraction of topics within a corpus;

FIG. 12 is a simplified block diagram of the navigation of ahierarchical abstraction of topics within a corpus;

FIG. 13 is a simplified block diagram of cognitive machine learningoperations to determine the prevalence of various terms within a corpusof content during a temporal sequence of events;

FIG. 14 is an inter-topic distance map depicting the distribution ofterms associated with a particular topic on a first date in a temporalsequence;

FIG. 15 is an inter-topic distance map depicting the distribution ofterms associated with a particular topic on a second date in a temporalsequence;

FIG. 16 is an inter-topic distance map depicting the distribution ofterms associated with a particular topic on a third date in a temporalsequence;

FIG. 17 is an inter-topic distance map depicting the distribution ofterms associated with a particular topic on a fourth date in a temporalsequence;

FIG. 18 is a simplified block diagram of the performance of continuouscognitive machine learning operations;

FIGS. 19 a through 19 c are a generalized flowchart of the performanceof continuous cognitive machine learning operations; and

FIGS. 20 a and 20 b are a simplified process flow diagram showing thegeneration of cognitive insights by a CILS.

DETAILED DESCRIPTION

A method, system and computer-usable medium are disclosed for cognitiveinference and learning operations. The present invention may be asystem, a method, and/or a computer program product. The computerprogram product may include a computer readable storage medium (ormedia) having computer readable program instructions thereon for causinga processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 is a generalized illustration of an information processing system100 that can be used to implement the system and method of the presentinvention. The information processing system 100 includes a processor(e.g., central processor unit or “CPU”) 102, input/output (I/O) devices104, such as a display, a keyboard, a mouse, and associated controllers,a hard drive or disk storage 106, and various other subsystems 108. Invarious embodiments, the information processing system 100 also includesnetwork port 110 operable to connect to a network 140, which is likewiseaccessible by a service provider server 142. The information processingsystem 100 likewise includes system memory 112, which is interconnectedto the foregoing via one or more buses 114. System memory 112 furthercomprises operating system (OS) 116 and in various embodiments may alsocomprise cognitive inference and learning system (CILS) 118. In theseand other embodiments, the CILS 118 may likewise comprise inventionmodules 120. In one embodiment, the information processing system 100 isable to download the CILS 118 from the service provider server 142. Inanother embodiment, the CILS 118 is provided as a service from theservice provider server 142.

In various embodiments, the CILS 118 is implemented to perform variouscognitive computing operations described in greater detail herein. Asused herein, cognitive computing broadly refers to a class of computinginvolving self-learning systems that use techniques such as spatialnavigation, machine vision, and pattern recognition to increasinglymimic the way the human brain works. To be more specific, earlierapproaches to computing typically solved problems by executing a set ofinstructions codified within software. In contrast, cognitive computingapproaches are data-driven, sense-making, insight-extracting,problem-solving systems that have more in common with the structure ofthe human brain than with the architecture of contemporary,instruction-driven computers.

To further differentiate these distinctions, traditional computers mustfirst be programmed by humans to perform specific tasks, while cognitivesystems learn from their interactions with data and humans alike, and ina sense, program themselves to perform new tasks. To summarize thedifference between the two, traditional computers are designed tocalculate rapidly. Cognitive systems are designed to quickly drawinferences from data and gain new knowledge.

Cognitive systems achieve these abilities by combining various aspectsof artificial intelligence, natural language processing, dynamiclearning, and hypothesis generation to render vast quantities ofintelligible data to assist humans in making better decisions. As such,cognitive systems can be characterized as having the ability to interactnaturally with people to extend what either humans, or machines, coulddo on their own. Furthermore, they are typically able to process naturallanguage, multi-structured data, and experience much in the same way ashumans. Moreover, they are also typically able to learn a knowledgedomain based upon the best available data and get better, and moreimmersive, over time.

It will be appreciated that more data is currently being produced everyday than was recently produced by human beings from the beginning ofrecorded time. Deep within this ever-growing mass of data is a class ofdata known as “dark data,” which includes neglected information, ambientsignals, and insights that can assist organizations and individuals inaugmenting their intelligence and deliver actionable insights throughthe implementation of cognitive applications. As used herein, cognitiveapplications, or “cognitive apps,” broadly refer to cloud-based, bigdata interpretive applications that learn from user engagement and datainteractions. Such cognitive applications extract patterns and insightsfrom dark data sources that are currently almost completely opaque.Examples of such dark data include disease insights from population-widehealthcare records and social media feeds, or from new sources ofinformation, such as sensors monitoring pollution in delicate marineenvironments.

Over time, it is anticipated that cognitive applications willfundamentally change the ways in which many organizations operate asthey invert current issues associated with data volume and variety toenable a smart, interactive data supply chain. Ultimately, cognitiveapplications hold the promise of receiving a user query and immediatelyproviding a data-driven answer from a masked data supply chain inresponse. As they evolve, it is likewise anticipated that cognitiveapplications may enable a new class of “sixth sense” applications thatintelligently detect and learn from relevant data and events to offerinsights, predictions and advice rather than wait for commands. Just asweb and mobile applications changed the way people access data,cognitive applications may change the way people listen to, and becomeempowered by, multi-structured data such as emails, social media feeds,doctors notes, transaction records, and call logs.

However, the evolution of such cognitive applications has associatedchallenges, such as how to detect events, ideas, images, and othercontent that may be of interest. For example, assuming that the role andpreferences of a given user are known, how is the most relevantinformation discovered, prioritized, and summarized from large streamsof multi-structured data such as news feeds, blogs, social media,structured data, and various knowledge bases? To further the example,what can a healthcare executive be told about their competitor's marketshare? Other challenges include the creation of acontextually-appropriate visual summary of responses to questions orqueries.

FIG. 2 is a simplified block diagram of a cognitive inference andlearning system (CILS) implemented in accordance with an embodiment ofthe invention. In various embodiments, the CILS 118 is implemented toincorporate a variety of processes, including semantic analysis 202,goal optimization 204, collaborative filtering 206, common sensereasoning 208, natural language processing 210, summarization 212,temporal/spatial reasoning 214, and entity resolution 216 to generatecognitive insights.

As used herein, semantic analysis 202 broadly refers to performingvarious analysis operations to achieve a semantic level of understandingabout language by relating syntactic structures. In certain embodiments,various syntactic structures are related from the levels of phrases,clauses, sentences and paragraphs, to the level of the body of contentas a whole, and to its language-independent meaning. In variousembodiments, the semantic analysis 202 process includes processing atarget sentence to parse it into its individual parts of speech, tagsentence elements that are related to predetermined items of interest,identify dependencies between individual words, and perform co-referenceresolution. For example, if a sentence states that the author reallylikes the hamburgers served by a particular restaurant, then the name ofthe “particular restaurant” is co-referenced to “hamburgers.”

As likewise used herein, goal optimization 204 broadly refers toperforming multi-criteria decision making operations to achieve a givengoal or target objective. In various embodiments, one or more goaloptimization 204 processes are implemented by the CILS 118 to definepredetermined goals, which in turn contribute to the generation of acognitive insight. For example, goals for planning a vacation trip mayinclude low cost (e.g., transportation and accommodations), location(e.g., by the beach), and speed (e.g., short travel time). In thisexample, it will be appreciated that certain goals may be in conflictwith another. As a result, a cognitive insight provided by the CILS 118to a traveler may indicate that hotel accommodations by a beach may costmore than they care to spend.

Collaborative filtering 206, as used herein, broadly refers to theprocess of filtering for information or patterns through thecollaborative involvement of multiple agents, viewpoints, data sources,and so forth. The application of such collaborative filtering 206processes typically involves very large and different kinds of datasets, including sensing and monitoring data, financial data, and userdata of various kinds. Collaborative filtering 206 may also refer to theprocess of making automatic predictions associated with predeterminedinterests of a user by collecting preferences or other information frommany users. For example, if person ‘A’ has the same opinion as a person‘B’ for a given issue ‘x’, then an assertion can be made that person ‘A’is more likely to have the same opinion as person ‘B’ opinion on adifferent issue ‘y’ than to have the same opinion on issue ‘y’ as arandomly chosen person. In certain embodiments, the collaborativefiltering 206 process is implemented with various recommendation enginesfamiliar to those of skill in the art to make recommendations.

As used herein, common sense reasoning 208 broadly refers to simulatingthe human ability to make deductions from common facts they inherentlyknow. Such deductions may be made from inherent knowledge about thephysical properties, purpose, intentions and possible behavior ofordinary things, such as people, animals, objects, devices, and so on.In various embodiments, common sense reasoning 208 processes areimplemented to assist the CILS 118 in understanding and disambiguatingwords within a predetermined context. In certain embodiments, the commonsense reasoning 208 processes are implemented to allow the CILS 118 togenerate text or phrases related to a target word or phrase to performdeeper searches for the same terms. It will be appreciated that if thecontext of a word is better understood, then a common senseunderstanding of the word can then be used to assist in finding betteror more accurate information. In various embodiments, this better ormore accurate understanding of the context of a word, and its relatedinformation, allows the CILS 118 to make more accurate deductions, whichare in turn used to generate cognitive insights.

As likewise used herein, natural language processing (NLP) 210 broadlyrefers to interactions with a system, such as the CILS 118, through theuse of human, or natural, languages. In various embodiments, various NLP210 processes are implemented by the CILS 118 to achieve naturallanguage understanding, which enables it to not only derive meaning fromhuman or natural language input, but to also generate natural languageoutput. Summarization 212, as used herein, broadly refers to processinga set of information, organizing and ranking it, and then generating acorresponding summary. As an example, a news article may be processed toidentify its primary topic and associated observations, which are thenextracted, ranked, and then presented to the user. In certainembodiments, various summarization 212 processes are implemented by theCILS 118 to generate summarizations of content streams, which are inturn used to generate cognitive insights.

As used herein, temporal/spatial reasoning 214 broadly refers toreasoning based upon qualitative abstractions of temporal and spatialaspects of common sense knowledge, described in greater detail herein.For example, it is not uncommon for a predetermined set of data tochange over time. Likewise, other attributes, such as its associatedmetadata, may likewise change over time. As a result, these changes mayaffect the context of the data. To further the example, the context ofasking someone what they believe they should be doing at 3:00 in theafternoon during the workday while they are at work may be quitedifferent than asking the same user the same question at 3:00 on aSunday afternoon when they are at home.

As likewise used herein, entity resolution 216 broadly refers to theprocess of finding elements in a set of data that refer to the sameentity across different data sources (e.g., structured, non-structured,streams, devices, etc.), where the target entity does not share a commonidentifier. In various embodiments, the entity resolution 216 process isimplemented by the CILS 118 to identify significant nouns, adjectives,phrases or sentence elements that represent various predeterminedentities within one or more domains. From the foregoing, it will beappreciated that the implementation of one or more of the semanticanalysis 202, goal optimization 204, collaborative filtering 206, commonsense reasoning 208, natural language processing 210, summarization 212,temporal/spatial reasoning 214, and entity resolution 216 processes bythe CILS 118 can facilitate the generation of a semantic, cognitivemodel.

In certain embodiments, the CILS 118 receives ambient signals 220,curated data 222, transaction data 224, and learned knowledge 226, whichis then processed by the CILS 118 to generate one or more cognitivegraphs 228. In turn, the one or more cognitive graphs 228 are furtherused by the CILS 118 to generate cognitive insight streams, which arethen delivered to one or more destinations 232, as described in greaterdetail herein. As used herein, ambient signals 220 broadly refer toinput signals, or other data streams, that may contain data providingadditional insight or context to the curated data 222, transaction data224, and learned knowledge 226 received by the CILS 118. For example,ambient signals may allow the CILS 118 to understand that a user iscurrently using their mobile device, at location ‘x’, at time ‘y’, doingactivity ‘z’. To continue the example, there is a difference between theuser using their mobile device while they are on an airplane versususing their mobile device after landing at an airport and walkingbetween one terminal and another.

In various embodiments, the curated data 222 may include structured,unstructured, social, public, private, streaming, device or other typesof data described in greater detail herein. In certain embodiments, thetransaction data 224 may include blockchain-associated data, describedin greater detail herein, smart contract data, likewise described ingreater detail herein, or any combination thereof. In variousembodiments, the learned knowledge 226 is based upon past observationsand feedback from the presentation of prior cognitive insight streamsand recommendations. In certain embodiments, the learned knowledge 226is provided via a feedback look that provides the learned knowledge 226in the form of a learning stream of data.

As likewise used herein, a cognitive graph 228 refers to arepresentation of expert knowledge, associated with individuals andgroups over a period of time, to depict relationships between people,places, and things using words, ideas, audio and images. As such, it isa machine-readable formalism for knowledge representation that providesa common framework allowing data and knowledge to be shared and reusedacross user, application, organization, and community boundaries. Invarious embodiments, the information contained in, and referenced by, acognitive graph 228 is derived from many sources (e.g., public, private,social, device), such as curated data 222 and transaction data 224. Incertain of these embodiments, the cognitive graph 228 assists in theidentification and organization of information associated with howpeople, places and things are related to one other. In variousembodiments, the cognitive graph 228 enables automated agents, describedin greater detail herein, to access the Web more intelligently,enumerate inferences through utilization of curated, structured data222, and provide answers to questions by serving as a computationalknowledge engine.

In certain embodiments, the cognitive graph 228 not only elicits andmaps expert knowledge by deriving associations from data, it alsorenders higher level insights and accounts for knowledge creationthrough collaborative knowledge modeling. In various embodiments, thecognitive graph 228 is a machine-readable, declarative memory systemthat stores and learns both episodic memory (e.g., specific personalexperiences associated with an individual or entity), and semanticmemory, which stores factual information (e.g., geo location of anairport or restaurant). For example, the cognitive graph 228 may knowthat a given airport is a place, and that there is a list of relatedplaces such as hotels, restaurants and departure gates. Furthermore, thecognitive graph 228 may know that people such as business travelers,families and college students use the airport to board flights fromvarious carriers, eat at various restaurants, or shop at certain retailstores.

In certain embodiments, the cognitive insight stream 230 isbidirectional, and supports flows of information both to and fromdestinations 232. In these embodiments, the first flow is generated inresponse to receiving a query, and subsequently delivered to one or moredestinations 232. The second flow is generated in response to detectinginformation about a user of one or more of the destinations 232. Suchuse results in the provision of information to the CILS 118. Inresponse, the CILS 118 processes that information, in the context ofwhat it knows about the user, and provides additional information to theuser, such as a recommendation. In various embodiments, the cognitiveinsight stream 230 is configured to be provided in a “push” streamconfiguration familiar to those of skill in the art. In certainembodiments, the cognitive insight stream 230 is implemented to usenatural language approaches familiar to skilled practitioners of the artto support interactions with a user.

In certain embodiments, the cognitive insight stream 230 may include astream of visualized insights. As used herein, visualized insightsbroadly refers to cognitive insights that are presented in a visualmanner, such as a map, an infographic, images, and so forth. In variousembodiments, these visualized insights may include certain cognitiveinsights, such as “What happened?”, “What do I know about it?”, “What islikely to happen next?”, or “What should I do about it?” In theseembodiments, the cognitive insight stream is generated by variouscognitive agents, which are applied to various sources, datasets, andcognitive graphs. As used herein, a cognitive agent broadly refers to acomputer program that performs a task with minimum specific directionsfrom users and learns from each interaction with data and human users.

In various embodiments, the CILS 118 delivers Cognition as a Service(CaaS). As such, it provides a cloud-based development and executionplatform that allow various cognitive applications and services tofunction more intelligently and intuitively. In certain embodiments,cognitive applications powered by the CILS 118 are able to think andinteract with users as intelligent virtual assistants. As a result,users are able to interact with such cognitive applications by askingthem questions and giving them commands. In response, these cognitiveapplications will be able to assist the user in completing tasks andmanaging their work more efficiently.

In these and other embodiments, the CILS 118 can operate as an analyticsplatform to process big data, and dark data as well, to provide dataanalytics through a public, private or hybrid cloud environment. As usedherein, cloud analytics broadly refers to a service model wherein datasources, data models, processing applications, computing power, analyticmodels, and sharing or storage of results are implemented within a cloudenvironment to perform one or more aspects of analytics.

In various embodiments, users submit queries and computation requests ina natural language format to the CILS 118. In response, they areprovided with a ranked list of relevant answers and aggregatedinformation with useful links and pertinent visualizations through agraphical representation. In these embodiments, the cognitive graph 228generates semantic and temporal maps to reflect the organization ofunstructured data and to facilitate meaningful learning from potentiallymillions of lines of text, much in the same way as arbitrary syllablesstrung together create meaning through the concept of language.

FIG. 3 is a simplified block diagram of a cognitive inference andlearning system (CILS) reference model implemented in accordance with anembodiment of the invention. In this embodiment, the CILS referencemodel is associated with the CILS 118 shown in FIG. 2 . As shown in FIG.3 , the CILS reference model includes client applications 302,application accelerators 306, a cognitive platform 310, and cloudinfrastructure 340. In various embodiments, the client applications 302include cognitive applications 304, which are implemented to understandand adapt to the user by natively accepting and understanding humanforms of communication, such as natural language text, audio, images,video, and so forth.

In these and other embodiments, the cognitive applications 304 possesssituational and temporal awareness based upon ambient signals from usersand data, which facilitates understanding the user's intent, content,context and meaning to drive goal-driven dialogs and outcomes. Further,they are designed to gain knowledge over time from a wide variety ofstructured, non-structured, transactional and device data sources,continuously interpreting and autonomously reprogramming themselves tobetter understand a given domain. As such, they are well-suited tosupport human decision making, by proactively providing trusted advice,offers and recommendations while respecting user privacy andpermissions.

In various embodiments, the application accelerators 306 include acognitive application framework 308. In certain embodiments, theapplication accelerators 306 and the cognitive application framework 308support various plug-ins and components that facilitate the creation ofclient applications 302 and cognitive applications 304. In variousembodiments, the application accelerators 306 include widgets, userinterface (UI) components, reports, charts, and back-end integrationcomponents familiar to those of skill in the art.

As likewise shown in FIG. 3 , the cognitive platform 310 includes amanagement console 312, a development environment 314, applicationprogram interfaces (APIs) 316, sourcing agents 318, a cognitive engine320, destination agents 336, platform data 338, and transaction data339, such as blockchain data, all of which are described in greaterdetail herein. In various embodiments, the development environment 314is implemented to create custom extensions to the CILS 118 shown in FIG.2 . In certain embodiments, the development environment 314 isimplemented for the development of a custom application, which maysubsequently be deployed in a public, private or hybrid cloudenvironment. In various embodiments, the development environment 314 isimplemented for the development of a custom sourcing agent, a custombridging agent, a custom destination agent, or various analyticsapplications or extensions.

In certain embodiments, the APIs 316 are implemented to build and managevarious cognitive applications 304, described in greater detail herein,which are then executed on the cognitive platform 310 to generatecognitive insights. Likewise, the sourcing agents 318 are implemented invarious embodiments to source a variety of multi-site, multi-structuredsource streams of data described in greater detail herein. In variousembodiments, the cognitive engine 320 includes a dataset engine 322, agraph query engine 326, an insight/learning engine 330, and foundationcomponents 334. In certain embodiments, the dataset engine 322 isimplemented to establish and maintain a dynamic data ingestion andenrichment pipeline. In these and other embodiments, the dataset engine322 may be implemented to orchestrate one or more sourcing agents 318 tosource data. Once the data is sourced, the data set engine 322 performsdata enriching and other data processing operations, described ingreater detail herein, and generates one or more sub-graphs that aresubsequently incorporated into a target cognitive graph.

In various embodiments, the graph query engine 326 is implemented toreceive and process queries such that they can be bridged into acognitive graph, as described in greater detail herein, through the useof a bridging agent. In certain embodiments, the graph query engine 326performs various natural language processing (NLP), familiar to skilledpractitioners of the art, to process the queries. In variousembodiments, the insight/learning engine 330 is implemented toencapsulate a particular algorithm, which is then applied to a cognitivegraph to generate a result, such as a recommendation or a cognitiveinsight, described in greater detail herein.

In certain embodiments, one or more such algorithms may contribute toanswering a specific question and provide additional recommendations,cognitive insights, or a combination thereof. In various embodiments,two or more of the dataset engine 322, the graph query engine 326, andthe insight/learning engine 330 may be implemented to operatecollaboratively or autonomously to generate a recommendation, acognitive insight, or a combination thereof. The foundation components334 shown in FIG. 3 may include various reusable components, familiar tothose of skill in the art, which may be used in certain embodiments toenable the dataset engine 322, the graph query engine 326, and theinsight/learning engine 330 to perform their respective operations andprocesses. Examples of such foundation components 334 include naturallanguage processing (NLP) components and core algorithms, such ascognitive algorithms.

In various embodiments, the platform data 338 includes various datarepositories, described in greater detail herein, that are accessed bythe cognitive platform 310 to generate cognitive insights. In certainembodiments, the transaction data 339 may include blockchain dataassociated with one or more public blockchains, one or more privateblockchains, or a combination thereof, as described in greater detailherein. In various embodiments, the platform data 338, or thetransaction data 339, or a combination thereof, is used to generate ablockchain-associated cognitive insight.

As used herein, a blockchain-associated cognitive insight broadly refersto a cognitive insight that is generated at least in part through theuse of blockchain data, or alternatively, provided in the form of ablockchain transaction, described in greater detail herein. As likewiseused herein, blockchain data broadly refers to any data associated witha given blockchain, whether it is related to the data structure of theblockchain as a whole or its individual elements, the individual dataelements it may contain, or its associated metadata. Likewise,blockchain data also broadly refers to the rules and parameters of acorresponding blockchain's operation, the protocols related to itsinteraction with applications and other blockchains, or itscorresponding Application Program Interface (API).

In certain embodiments, the destination agents 336 are implemented topublish cognitive insights to a consumer of cognitive insight data.Examples of such consumers of cognitive insight data include targetdatabases, public or private blockchains, business intelligenceapplications, and mobile applications. It will be appreciated that manysuch examples of cognitive insight data consumers are possible.Accordingly, the foregoing is not intended to limit the spirit, scope orintent of the invention. In certain embodiments, as described in greaterdetail herein, the cloud infrastructure 340 may include cognitive cloudmanagement 342 components and analytics infrastructure 344 components.

FIG. 4 is a simplified process diagram of cognitive inference andlearning system (CILS) operations performed in accordance with anembodiment of the invention. In various embodiments, these CILSoperations may include a perceive 406 phase, a relate 408 phase, anoperate 410 phase, a process and execute 412 phase, and a learn 414phase. In these and other embodiments, the CILS 118 shown in FIG. 2 isimplemented to mimic cognitive processes associated with the humanbrain. In various embodiments, the CILS operations are performed throughthe implementation of a cognitive platform 310, described in greaterdetail herein. In these and other embodiments, the cognitive platform310 may be implemented within a cloud analytics infrastructure 344,which in turn is implemented within a cloud infrastructure 340, likewisedescribed in greater detail herein.

In various embodiments, multi-site, multi-structured source streams 404are provided by sourcing agents, as described in greater detail herein.In these embodiments, the source streams 404 are dynamically ingested inreal-time during the perceive 406 phase, and based upon a predeterminedcontext, extraction, parsing, and tagging operations are performed onlanguage, text and images contained in the source streams 404. Automaticfeature extraction and modeling operations are then performed with thepreviously processed source streams 404 during the relate 408 phase togenerate queries to identify related data (i.e., corpus expansion).

In various embodiments, operations are performed during the operate 410phase to discover, summarize and prioritize various concepts, which arein turn used to generate actionable recommendations and notificationsassociated with predetermined plan-based optimization goals. Theresulting actionable recommendations and notifications are thenprocessed during the process and execute 412 phase to provide cognitiveinsights, such as recommendations, to various predetermined destinationsand associated application programming interfaces (APIs) 424.

In various embodiments, features from newly-observed data areautomatically extracted from user feedback during the learn 414 phase toimprove various analytical models. In these embodiments, the learn 414phase includes feedback on observations generated during the relate 408phase, which is provided to the perceive 406 phase. Likewise, feedbackon decisions resulting from operations performed during the operate 410phase, and feedback on results resulting from operations performedduring the process and execute 412 phase, are also provided to theperceive 406 phase.

In various embodiments, user interactions result from operationsperformed during the process and execute 412 phase. In theseembodiments, data associated with the user interactions are provided tothe perceive 406 phase as unfolding interactions 422, which includeevents that occur external to the CILS operations described in greaterdetail herein. As an example, a first query from a user may be submittedto the CILS system, which in turn generates a first cognitive insight,which is then provided to the user. In response, the user may respond byproviding a first response, or perhaps a second query, either of whichis provided in the same context as the first query. The CILS receivesthe first response or second query, performs various CILS operations,and provides the user a second cognitive insight. As before, the usermay respond with a second response or a third query, again in thecontext of the first query. Once again, the CILS performs various CILSoperations and provides the user a third cognitive insight, and soforth. In this example, the provision of cognitive insights to the user,and their various associated responses, results in unfoldinginteractions 422, which in turn result in a stateful dialog that evolvesover time. Skilled practitioners of the art will likewise realize thatsuch unfolding interactions 422, occur outside of the CILS operationsperformed by the cognitive platform 310.

FIG. 5 depicts the lifecycle of cognitive inference and learning system(CILS) agents implemented in accordance with an embodiment of theinvention to perform CILS operations. In various embodiments, the CILSagents lifecycle 502 may include implementation of a sourcing 504 agent,an enrichment 506 agent, a bridging 508 agent, an insight 510 agent, adestination 512 agent, and a learning 514 agent. In these embodiments,the sourcing 504 agent is implemented to source a variety of multi-site,multi-structured source streams of data described in greater detailherein. In certain embodiments, the sourcing c04 agent may include abatch upload agent, an Application Program Interface (API) connectorsagent, a real-time streams agent, a Structured Query Language (SQL)/NotOnly SQL (NoSQL) databases agent, a message engines agent, a blockchainsourcing agent, one or more custom sourcing agents, or some combinationthereof. Skilled practitioners of the art will realize that other typesof sourcing agents 504 may be used in various embodiments.

These sourced data streams are then provided to an enrichment 506 agent,which then invokes an enrichment component to perform enrichmentoperations familiar to those of skill in the art to generate enricheddata streams. As an example, a data stream may be sourced fromAssociated Press® by a sourcing agent 504 and provided to an enrichmentcomponent. In turn, the enrichment component may enrich the data streamby performing sentiment analysis, geotagging, and entity detectionoperations. In certain embodiments, the enrichment operations includefiltering operations familiar to skilled practitioners of the art. Tofurther the preceding example, the Associated Press® data stream may befiltered by a predetermined geography attribute to generate an enricheddata stream.

The enriched data streams are then provided to a bridging 508 agent,which is used to perform bridging operations. In various embodiments,the bridging operations are performed to provide domain-specificresponses when bridging a translated query to a target cognitive graph.For example, the same query bridged to various cognitive graphs mayresult in different answers for different domains. In certainembodiments, the bridging operations are implemented to process what isknown about the translated query, in the context of the user, to providean answer that is relevant to a specific domain.

As an example, a user may ask, “Where should I eat today?” If the userhas been prescribed a particular health regimen, the bridging operationsmay result in a suggestion for a restaurant with a “heart healthy” menu.However, if the user is a business traveler, the bridging operations mayresult in a suggestion for the nearest restaurant that has the user'sfavorite food. In various embodiments, performance of the bridgingoperations may result in the provision of answers or suggestions thatare composed and ranked according to a specific domain of use.

The results of the bridging operations are in turn provided to aninsight 510 agent, which is implemented in certain embodiments to createa visual data story, highlighting user-specific insights, relationshipsand recommendations. In various embodiments, insight agents 510 use aparticular cognitive graph, described in greater detail herein, as adata source to generate individual cognitive insights. In certainembodiments, the cognitive graph may be implemented as an applicationcognitive graph, likewise described in greater detail herein.

The resulting visual data story is then provided to a destination 512agent, which is implemented in various embodiments to publish cognitiveinsights to a consumer of cognitive insight data. Examples of suchconsumers of cognitive insight data include target databases, public orprivate blockchains, business intelligence applications, and mobileapplications. In various embodiments, destination agents 512 may includea Hypertext Transfer Protocol (HTTP) stream agent, an API connectorsagent, a databases agent, a message engines agent, a mobile pushnotification agent, a blockchain destination agents, custom destinationagents, or some combination thereof.

In response to receipt of the visual data story, the consumer ofcognitive insight data provides feedback to a learning 514 agent, whichis implemented in certain embodiments to provide the feedback to thesourcing agent 504, at which point the CILS agents lifecycle 502 iscontinued. In various embodiments, a learning agent 514 is implementedto work in the background to continually update a cognitive graph, asdescribed in greater detail herein, from each unique interaction withdata and users. From the foregoing, skilled practitioners of the artwill recognize that each iteration of the cognitive agents lifecycle 502provides more informed cognitive insights.

FIG. 6 depicts a cognitive learning framework implemented in accordancewith an embodiment of the invention to perform cognitive learningoperations. As used herein, a cognitive learning operation broadlyrefers to the implementation of a cognitive learning technique,described in greater detail herein, to generate a cognitive learningresult. In various embodiments, the implementation of the learningtechnique is performed by a Cognitive Inference and Learning System(CILS), likewise described in greater detail herein. In certainembodiments, the cognitive learning result is used by the CILS to updatea knowledge model. In various embodiments, the knowledge model isimplemented as a universal knowledge repository. In certain embodiments,the knowledge model is implemented as a cognitive graph.

In various embodiments, the cognitive learning framework 600 may includevarious cognitive learning styles 602 and cognitive learning categories610. As used herein, a cognitive learning style broadly refers to ageneralized learning approach implemented by a CILS to perform acognitive learning operation. In certain embodiments, the cognitivelearning styles 602 may include a declared 604 cognitive learning style,an observed 606 cognitive learning style, and an inferred 608 cognitivelearning style. As likewise used herein, a declared 604 cognitivelearning style broadly refers to the use of declarative data by a CILSto perform a corresponding cognitive learning operation. In variousembodiments, the declarative data may be processed by the CILS as astatement, an assertion, or a verifiable fact. For example, anelectronic medical record (EMR) may contain declarative data assertingthat John Smith has Type 1 diabetes, which is a verifiable fact.

Likewise, as used herein, an observed 806 cognitive learning stylebroadly refers to the use of observed data by CILS to perform acorresponding cognitive learning operation. In various embodiments, theobserved data may include a pattern, a concept, or some combinationthereof. As an example, a CILS may receive and process a stream ofinformation, and over time observe the formation of a discernablepattern, such as a user always ordering Chinese or Thai food fordelivery at lunchtime. In this example, the discerned pattern of theuser's ordering behavior may correspond to the concept that the user'slunchtime food preference is Asian cuisine.

In certain embodiments, a concept may include an observation of the useof certain words in a particular context. For example, the use of theword “haircut” in a financial text may refer to the difference betweenthe market value of an asset used as loan collateral and the amount ofthe loan, as opposed to a service performed by a hair stylist. In thisexample, natural language processing (NLP) approaches known to those ofskill in the art are implemented by the CILS during the performance ofcognitive learning operations to determine the context in which the word“haircut” is used.

As likewise used herein, an inferred 608 cognitive learning stylebroadly refers to the use of inferred data by a CILS to perform acorresponding cognitive learning operation. In various embodiments theinferred data may include data inferred from the processing of sourcedata. In certain embodiments, the inferred data may include conceptsthat are inferred from the processing of other concepts. In theseembodiments, the inferred data resulting from the processing of thesource data, the concepts, or a combination thereof, may result in theprovision of new information that was not in the source data or otherconcepts.

As an example, a user's selection of a particular accommodation in aresort area during a holiday may result in an inference they preferstaying at a bed and breakfast while on personal travel. Likewise, theselection of a four star accommodation in a downtown area on a weekdaymay result in an inference the same user prefers a luxury hotel while onbusiness travel. In this example, the user may not declaratively statean accommodation preference for a given type of travel.

As used herein, a cognitive learning category 610 broadly refers to asource of information used by a CILS to perform cognitive learningoperations. In various embodiments, the cognitive learning categories610 may include a data-based 612 cognitive learning category and aninteraction-based 614 cognitive learning category. As likewise usedherein, a data-based 612 cognitive learning category broadly refers tothe use of data as a source of information in the performance of acognitive learning operation by a CILS. In certain embodiments, the datamay be provided to the CILS in real-time, near real-time, or batch modeas it is performing cognitive learning operations. In variousembodiments, the data may be provided to the CILS as a result of a querygenerated by the CILS.

In certain embodiments, the data is provided to the CILS by a cognitiveagent, described in greater detail herein. In one embodiment, thecognitive agent is a learning agent, likewise described in greaterdetail herein. In various embodiments, the data may be multi-structureddata. In these embodiments, the multi-structured data may includeunstructured data (e.g., a document), semi-structured data (e.g., asocial media post), and structured data (e.g., a string, an integer,etc.), such as data stored in a relational database management system(RDBMS). In certain embodiments, the data may be public, proprietary, ora combination thereof.

As likewise used herein, an interaction-based 614 cognitive learningcategory broadly refers to the use of one or more results of aninteraction as a source of information used by a CILS to perform acognitive learning operation. In various embodiments, the interactionmay be between any combination of devices, applications, services,processes, or users. In certain embodiments, the results of theinteraction may be provided in the form of feedback data to the CILS.

In various embodiments, the interaction may be explicitly or implicitlyinitiated by the provision of input data to the devices, applications,services, processes or users. In certain embodiments, the input data maybe provided in response to a cognitive insight provided by a CILS. Inone embodiment, the input data may include a user gesture, such as a keystroke, mouse click, finger swipe, or eye movement. In anotherembodiment, the input data may include a voice command from a user. Inyet another embodiment, the input data may include data associated witha user, such as biometric data (e.g., retina scan, fingerprint, bodytemperature, pulse rate, etc.).

In yet still another embodiment, the input data may includeenvironmental data (e.g., current temperature, etc.), location data(e.g., geographical positioning system coordinates, etc.), device data(e.g., telemetry data, etc.), transaction data (e.g., transaction dataassociated with a blockchain), or other data provided by a device,application, service, process or user. Those of skill in the art willrealize that many such embodiments of cognitive learning styles 602 andcognitive learning categories 610 are possible. Accordingly, theforegoing is not intended to limit the spirit, scope or intent of theinvention.

As used herein, a cognitive learning technique refers to the use of acognitive learning style, in combination with a cognitive learningcategory, to perform a cognitive learning operation. In variousembodiments, individual cognitive learning techniques associated with aprimary cognitive learning style are respectively bounded by anassociated primary cognitive learning category. For example, as shown inFIG. 6 , the direct correlations 624 and explicit likes/dislikes 626cognitive learning techniques are both associated with the declared 604learning style and respectively bounded by the data-based 612 andinteraction-based 608 cognitive learning categories.

As likewise shown in FIG. 6 , the patterns and concepts 628 and behavior830 cognitive learning techniques are both associated with the observed606 cognitive learning style and likewise respectively bounded by thedata-based 612 and interaction-based 614 cognitive learning categories.Likewise, as shown in FIG. 6 , the concept entailment 632 and contextualrecommendation 634 cognitive learning techniques are both associatedwith the inferred 608 cognitive learning style and likewise respectivelybounded by the data-based 612 and interaction-based 614 cognitivelearning categories.

As used herein, a direct correlations 624 cognitive learning techniquebroadly refers to the implementation of a declared 604 cognitivelearning style, bounded by a data-based 612 cognitive learning category,to perform cognitive learning operations related to direct correlations.Examples of direct correlation include statistical relationshipsinvolving dependence, such as the correlation between the stature orother physical characteristics of parents and their biologicaloffspring. As likewise used herein, an explicit likes/dislikes 624cognitive learning technique broadly refers to the implementation of adeclared 612 cognitive learning style, bounded by an interaction-based606 cognitive learning category, to perform cognitive learningoperations related to a user's explicit likes/dislikes.

In various embodiments, a user's explicit likes/dislikes may bedeclaratively indicated through the receipt of user input data,described in greater detail herein. For example, an online shopper mayselect a first pair of shoes that are available in a white, black andbrown. The user then elects to view a larger photo of the first pair ofshoes, first in white, then in black, but not brown. To continue theexample, the user then selects a second pair of shoes that are likewiseavailable in white, black and brown. As before, the user elects to viewa larger photo of the second pair of shoes, first in white, then inblack, but once again, not brown. In this example, the user's onlineinteraction indicates an explicit like for white and black shoes and anexplicit dislike for brown shoes.

As used herein, a patterns and concepts 628 cognitive learning techniquebroadly refers to the implementation of an observed 612 cognitivelearning style, bounded by a data-based 604 cognitive learning category,to perform cognitive learning operations related to the observation ofpatterns and concepts. As an example, a database record may includeinformation related to various transactions associated with a user. Inthis example, a pattern may be observed within the transactions that theuser always uses rental cars when traveling between cities inCalifornia, but always uses trains when traveling between cities in NewYork, New Jersey, or Pennsylvania. By extension, this pattern maycorrespond to a concept that the user prefers automobile transportationwhen traveling between cities on the West coast, but prefers traintransportation when traveling between cities on the East coast.

As used herein, a behavior 630 cognitive learning technique broadlyrefers to the implementation of an observed 612 cognitive learningstyle, bounded by an interaction-based 608 cognitive learning category,to perform cognitive learning operations related to observed behaviors.In various embodiments, the observed behavior associated with aninteraction corresponds to various input data, likewise described ingreater detail herein. In certain embodiments, the observed behaviorsmay include observed behavior associated with interactions, described ingreater detail herein. For example, a user may consistently place anonline order for Mexican, Thai or Indian food to be delivered to theirhome in the evening. To continue the example, promotional offers forfried chicken or seafood are consistently ignored in the evening, yetconsistently accepted at lunchtime. Furthermore, the observed behaviorof the user is to accept the promotional offer that provides the mostfood at the lowest cost. In this example, the user's observed onlinebehavior indicates a preference for spicy food in the evenings,regardless of price. Likewise, the user's observed online behavior mayindicate a preference for low cost, non-spicy foods for lunch.

As used herein, a concept entailment 632 cognitive learning techniquebroadly refers to the implementation of an inferred 608 cognitivelearning style, bounded by a data-based 604 cognitive learning category,to perform cognitive learning operations related to concept entailment.As likewise used herein, concept entailment broadly refers to theconcept of understanding language, within the context of one piece ofinformation being related to another. For example, if a statement ismade that implies ‘x’, and ‘x is known to imply ‘y’, then by extension,the statement may imply ‘y’ as well. In this example, there is achaining of evidence between the statement, ‘x’, and ‘y’ that may resultin a conclusion supported by the chain of evidence.

As used herein, a contextual recommendation 634 cognitive learningtechnique broadly refers to the implementation of an inferred 608cognitive learning style, bounded by an interaction-based 614 cognitivelearning category, to perform cognitive learning operations related tocontextual recommendations provided to a user. As likewise used herein,a contextual recommendation broadly refers to a recommendation made to auser based upon a particular context. As an example, a user may performan online search for a casual, affordable restaurant that is nearby. Tocontinue the example, the user is currently on a low-sodium, gluten-freediet that has been prescribed by their healthcare provider.Additionally, the healthcare provider has recommended that the user walkat least two miles every day.

To further continue the example, there may be five casual, affordablerestaurants that are in close proximity to the location coordinatesprovided by the user's mobile device, all of which are presented to theuser for consideration. In response, the user further requests distanceinformation to each of the restaurants, followed by a request to showonly those restaurants offering low-sodium, gluten free menu items. As aresult of the user interaction, the CILS responds with directions to theonly restaurant offering low-sodium, gluten-free dishes. Further, theCILS may recommend the user try a Mediterranean dish, as pastinteractions has indicated that the user enjoys Mediterranean cuisine.In this example, the contextual recommendation is inferred from a seriesof interactions with the user.

In various embodiments, machine learning algorithms 616 are respectivelyimplemented with a cognitive learning technique by a CILS whenperforming cognitive learning operations. In one embodiment, asupervised learning 618 machine learning algorithm may be implementedwith a direct correlations 624 cognitive learning technique, an explicitlikes/dislikes 626 cognitive learning technique, or both. In anotherembodiment, an unsupervised learning 620 machine learning algorithm maybe implemented with a patterns and concepts 628 cognitive learningtechnique, a behavior 630 cognitive learning technique, or both. In yetanother embodiment, a probabilistic reasoning 622 machine learningalgorithm may be implemented with a concept entailment 632 cognitivelearning technique, a contextual recommendation 634 cognitive learningtechnique, or both. Skilled practitioners of the art will recognize thatmany such embodiments are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

As used herein, a supervised learning 618 machine learning algorithmbroadly refers to a machine learning approach for inferring a functionfrom labeled training data. The training data typically consists of aset of training examples, with each example consisting of an inputobject (e.g., a vector) and a desired output value (e.g., a supervisorysignal). In various embodiments, a supervised learning 618 algorithm isimplemented to analyze the training data and produce an inferredfunction, which can be used for mapping new examples. As likewise usedherein, an unsupervised learning 620 machine learning algorithm broadlyrefers to a machine learning approach for finding non-obvious or hiddenstructures within a set of unlabeled data. In various embodiments, theunsupervised learning 620 machine learning algorithm is not given a setof training examples. Instead, it attempts to summarize and explain keyfeatures of the data it processes.

Examples of unsupervised learning approaches include clustering (e.g.,k-means, mixture models, hierarchical clustering, etc.) and latentvariable models (e.g., expectation-maximization algorithms, method ofmoments, blind signal separation techniques, etc.). Likewise, as usedherein, a probabilistic reasoning 622 machine learning algorithm broadlyrefers to a machine learning approach that combines the ability ofprobability theory to handle uncertainty with the ability of deductivelogic to exploit structure. More specifically, probabilistic reasoningattempts to find a natural extension of traditional logic truth tables.The results they define are derived through probabilistic expressionsinstead.

In various embodiments, reinforcement learning 636 approaches areimplemented by a CILS in combination with a patterns and concepts 628, abehavior 630, a concept entailment 632, or a contextualizationrecommendation 634 cognitive learning technique when performingcognitive learning operations. As used herein, reinforcement learningbroadly refers to machine learning approaches inspired by behavioristpsychology, where software agents take actions within an environment tomaximize a notion of cumulative reward. Those of skill in the art willbe familiar with such reinforcement approaches, which are commonly usedin game theory, control theory, operations research, information theory,simulation-based optimization, multi-agent systems, swarm intelligence,statistics, and genetic algorithms.

In certain embodiments, a particular cognitive learning technique mayinclude the implementation of certain aspects of a secondary cognitivelearning style, aspects of a secondary learning category, or acombination thereof. As an example, the patterns and concepts 628cognitive learning technique may include implementation of certainaspects of the direct correlations 624 and concept entailment 632cognitive learning techniques, and by extension, implementation ofcertain aspects of the declared 804 and inferred 608 cognitive learningstyles. In various embodiments, the data-based 612 cognitive learningcategory, machine learning algorithms 618, and the interaction-based 614cognitive learning category are respectively associated with the source640, process 642 and deliver 644 steps of a cognitive learning process.

As used herein, a cognitive learning process broadly refers to a seriesof cognitive learning steps performed by a CILS to generate a cognitivelearning result. As likewise used herein, a source 640 step of acognitive learning process broadly refers to operations associated withthe acquisition of data used by a CILS to perform a cognitive learningoperation. Likewise, as used herein, a process 642 step of a cognitivelearning process broadly refers to the use of individual machinelearning algorithms 616 by a CILS to perform cognitive learningoperations. As likewise used herein, a deliver 644 step of a cognitivelearning process broadly refers to the delivery of a cognitive insight,which results in an interaction, described in greater detail herein.Information related to, or resulting from, the interaction is then usedby a CILS to perform cognitive learning operations.

In various embodiments, the cognitive insight is delivered to a device,an application, a service, a process, a user, or a combination thereof.In certain embodiments, the resulting interaction information islikewise received by a CLLS from a device, an application, a service, aprocess, a user, or a combination thereof. In various embodiments, theresulting interaction information is provided in the form of feedbackdata to the CILS. In these embodiments, the method by which thecognitive learning process, and its associated cognitive learning steps,is implemented is a matter of design choice. Skilled practitioners ofthe art will recognize that many such embodiments are possible.Accordingly, the foregoing is not intended to limit the spirit, scope orintent of the invention.

FIGS. 7 a and 7 b are a simplified block diagram of a Cognitive Learningand Inference System (CILS) implemented in accordance with an embodimentof the invention to manage the performance of cognitive learningoperations throughout their lifecycle. In various embodiments,individual elements of a CILS are implemented within a massivelyparallel and portable cloud insights fabric 702. In this embodiment, theindividual elements of the CILS include repositories of multi-structureddata 704, a universal knowledge repository 718, various shared analyticsservices 730, a deep cognition engine 744, and a cognitive insights as aservice 746 module.

In certain embodiments, the repositories of multi-structured data 704may include public 706, proprietary 708, social 710, device 712, andother types of data. Examples of such data include emails, social mediafeeds, news feeds, blogs, doctor's notes, transaction records,blockchain transactions, call logs, and device telemetry streams. Inthese embodiments, the repositories of multi-structured data 704 mayinclude unstructured data (e.g., a document), semi-structured data(e.g., a social media post), and structured data (e.g., a string, aninteger, etc.), such as data stored in a relational database managementsystem (RDBMS). In various embodiments, such data may be stored in adata lake 714, a data warehouse 716, a blockchain 717, or somecombination thereof.

As shown in FIG. 7 b , the universal knowledge repository 718 mayinclude various cognitive agents 720, described in greater detailherein, data subscription services 722, and a cognitive knowledge model724. In certain embodiments, the cognitive agents 720 include a learningagent. As likewise shown in FIG. 7 , the universal knowledge repositoryalso includes a fault-tolerant data compute architecture 726, familiarto those of skill in the art, and a data sovereignty, security, lineageand traceability system 728.

In various embodiments, individual data subscription services 722 areimplemented to deliver 756 data on an event-driven basis to the variousshared analytics services 730. In these embodiments, the data providedto the shared analytics services 730 is retrieved from the cognitiveknowledge model 724. In certain embodiments, the cognitive knowledgemodel 724 is implemented as one or more cognitive graphs. In variousembodiments, the cognitive graph may be implemented as an applicationcognitive graph, a cognitive session graph, a cognitive persona, or acognitive profile, all of which are described in greater detail herein.The method by which the data is provided to the shared analyticsservices 730 by the individual data subscription services 722 is amatter of design choice.

In certain embodiments, the fault-tolerant data compute architecture 726is implemented to provide an operational framework capable of reliablysupporting the other elements of the universal knowledge repository 718.In these embodiments, fault-tolerant approaches familiar to those ofskill in the art are implemented to accommodate needs to perform variouscognitive learning operations described in greater detail herein. Themethod by which these approaches are implemented is a matter of designchoice.

In various embodiments, the data sovereignty, security, lineage andtraceability system 728 is implemented to ensure that data ownershiprights are observed, data privacy is safeguarded, and data integrity isnot compromised. In certain embodiments, data sovereignty, security,lineage and traceability system 728 is likewise implemented to provide arecord of not only the source of the data throughout its lifecycle, butalso how it has been used, by whom, and for what purpose. Those of skillin the art will recognize many such embodiments are possible.Accordingly, the foregoing is not intended to limit the spirit, scope orintent of the invention.

In this embodiment, the shared analytics services 730 includes NaturalLanguage Processing (NLP) 732 services, development services 734,models-as-a-service 736, management services 738, profile services 740,and ecosystem services 742. In various embodiments, the NLP 732 servicesinclude services related to the provision and management of NLPapproaches and processes known to skilled practitioners of the art. Inthese embodiments, NLP 732 services are implemented by a CILS during theperformance of cognitive learning operations, as described in greaterdetail herein. The method by which individual NLP 732 services areimplemented by the CILS is a matter of design choice.

In certain embodiments, the development services 734 include servicesrelated to the management of data and models as they relate to thedevelopment of various analytic approaches known skilled practitionersof the art. In various embodiments, the models-as-a-service 736 includesservices for the management and provision of a model. In certainembodiments, the models as a service 736 may be implemented to createand provide a model composed of other models. In this embodiment, themethod by which the models-as-a-service 736 is implemented to create andprovide such a composite model is a matter of design choice. In variousembodiments, the management services 738 include services related to themanagement and provision of individual services associated with, or apart of, the shared analytics services 730.

In certain embodiments, the profile services 740 include servicesrelated to the provision and management of cognitive personas andcognitive profiles, described in greater detail herein, used by a CILSwhen performing a cognitive learning operation. In various embodiments,a cognitive identity management module 749 is implemented to accesscognitive persona and cognitive profile information associated with auser. In certain embodiments, the cognitive identity management module749 is implemented to verify the identity of a particular user. Invarious embodiments, provision of cognitive insights, or compositecognitive insights, results in the CILS receiving feedback 758 data fromvarious individual users and other sources, such as cognitiveapplications 748. Skilled practitioners of the art will recognize thatmany such embodiments are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

In various embodiments, the deep cognition engine 744 is implemented toprovide deep contextual understanding and interpretation as variouscognitive learning operations, described in greater detail herein, arebeing performed by a CILS. In certain embodiments, the deep cognitionengine 744 may include a perceive 506 phase, a relate 508 phase, anoperate 510 phase, a process and execute 512 phase, and a learn 514phase. In various embodiments, streams of data are sourced from therepositories of multi-structured data 704 are delivered 756 by sourcingagents, described in greater detail herein to the deep cognition engine744. In these embodiments, the source streams of data are dynamicallyingested in real-time during the perceive 506 phase, and based upon aparticular context, extraction, parsing, and tagging operations areperformed on language, text and images contained therein.

Automatic feature extraction and modeling operations are then performedwith the previously processed source streams of data during the relate508 phase to generate queries to identify related data. In variousembodiments, cognitive learning operations are performed during theoperate 510 phase to discover, summarize and prioritize variousconcepts, described in greater detail herein, which are in turn used togenerate actionable recommendations and notifications associated. Theresulting actionable recommendations and notifications are thenprocessed during the process and execute 512 phase to deliver 756cognitive insights, such as recommendations, to the cognitive insightsas a service 746 module.

In various embodiments, features from newly-observed data areautomatically extracted from user interaction 750 during the learn 514phase to improve various analytical models. In these embodiments, thelearn 514 phase includes feedback 758 data associated with observationsgenerated during the relate 508 phase, which is provided to the perceive506 phase. Likewise, feedback 758 data on decisions resulting fromoperations performed during the operate 510 phase, and feedback 758 datarelated to results resulting from operations performed during theprocess and execute 512 phase, are also provided to the perceive 506phase.

In various embodiments, user interactions 750 result from operationsperformed during the process and execute 512 phase. In theseembodiments, data associated with the user interactions 750 is providedas feedback 758 data to the perceive 506 phase. As an example, a firstquery from a user may be submitted to the CILS system, which in turngenerates a first cognitive insight, which is then provided to the user.In response, the user may respond by providing a first response, orperhaps a second query, either of which is provided in the same contextas the first query. The CILS receives the first response or secondquery, performs various cognitive learning operations, and provides theuser a second cognitive insight. As before, the user may respond with asecond response or a third query, in the context of the first or secondquery. Once again, the CILS performs various cognitive learningoperations and provides the user a third cognitive insight, and soforth.

In various embodiments, data may be delivered 756 from the repositoriesof multi-structured data 704 to the universal knowledge repository 718,which in turn may deliver 756 data to individual shared analyticsservices 730. In turn, individual shared analytics services 730 maydeliver 756 resulting data to the deep cognition engine 744. Likewise,the deep cognition engine 744 may in turn deliver 756 data to thecognitive insights as a service 746. In turn, the cognitive insights asa service 746 module may deliver data to various cognitive applications748.

In certain embodiments, the data delivered 756 by the cognitive insightsas a service 746 to the various cognitive applications 748 includesblockchain-associated cognitive insights, described in greater detailherein. In various embodiments, the various cognitive applications 748may provide data, including blockchain-associated cognitive insights andcomposite cognitive insights for interaction 750, described in greaterdetail herein. In certain embodiments, the interaction may include userinteraction resulting in the provision of user input data, likewisedescribed in greater detail herein.

In various embodiments, the interaction results in the provision offeedback 758 data to the various cognitive applications 748, where itmay be provided as feedback 758 data to the cognitive insights as aservice 746 module. Likewise, the cognitive insights as a service 746module may provide resulting feedback 758 data to the deep cognitionengine 744 for processing. In turn, the deep cognition engine 744 mayprovide resulting feedback 758 data to individual shared analyticsservices 730, which likewise may provide resulting feedback 758 data tothe universal knowledge repository 718.

In certain embodiments, the feedback 758 data provided to the universalknowledge repository 718 is used, as described in greater detail herein,to update the cognitive knowledge model 724. In various embodiments, theuniversal knowledge repository 718 may likewise provide feedback 758data to various repositories of multi-structured data 704. In certainembodiments, the feedback 758 data is used to update repositories ofmulti-structured data 704. In these embodiments, the feedback 758 datamay include updated data, new data, metadata, or a combination thereof.

In various embodiments, a first CILS element may iteratively deliver 756data to, and receive resulting feedback 758 data from, a second CILSelement prior to the second CILS element delivers data to a third CILSelement. As an example, the universal knowledge repository 718 maydeliver 756 a first set of data to the NLP services 732, which resultsin a first set of feedback 758 data being returned to the universalknowledge repository 718. As a result of receiving the first set offeedback 758 data, the universal knowledge repository 718 may provide asecond set of data to the models-as-a-service 736, which results in thegeneration of a second set of data. In this example, the second set ofdata is then delivered 756 to the deep cognition engine 744.

In one embodiment, the feedback 758 data received as a result of aninteraction 750 is provided to each of the individual CILS elements. Inanother embodiment, feedback 758 data received from one CILS element ismodified before it is provided as modified feedback 758 data to anotherCILS element. In yet another embodiment, feedback 758 data received fromone CILS element is not modified before it is provided as unmodifiedfeedback 758 data to another CILS element. Skilled practitioners willrecognize that many such embodiments are possible. Accordingly, theforegoing is not intended to limit the spirit, scope or intent of theinvention.

In various embodiments, the CILS is implemented to manage the lifecycle760 of a cognitive learning operation. In this embodiment, the cognitivelearning operation lifecycle 760 includes a source 762, a learn 765, aninfer 766, an interpret 768 and an act 770 lifecycle phase. As shown inFIG. 7 , the source 762, the learn 765, the infer 766, the interpret768, and act 770 lifecycle phases can interact with one another byproviding and receiving data between adjacent phases. In addition, theact 770 phase can provide data to the source 762 phase. In certainembodiments, the data the act 707 phase provides to the source 762 phaseincluded feedback data resulting from an interaction, described ingreater detail herein.

In various embodiments, the source 762 lifecycle phase is implemented toacquire data from the repositories of multi-structured data 704, whichin turn is provided to the universal knowledge repository 718. In oneembodiment, the data is provided to the cognitive knowledge model 724via the implementation of the fault-tolerant data compute architecture726. In another embodiment, the data sovereignty, security, lineage andtraceability system 728 is implemented to ensure that data ownershiprights are observed, data privacy is safeguarded, and data integrity isnot compromised during the source 762 lifecycle phase. In certainembodiments, data sovereignty, security, lineage and traceability system728 is likewise implemented to provide a record of not only the sourceof the data throughout its lifecycle, but also how it has been used, bywhom, and for what purpose.

In various embodiments, the learn 764 lifecycle phase is implemented tomanage cognitive learning operations being performed by a CILS, asdescribed in greater detail herein. In certain embodiments, cognitiveagents 720 are used in the performance of these cognitive learningoperations. In one embodiment, a learning agent is used in theperformance of certain cognitive learning operations, as described ingreater detail herein. In various embodiments, the infer 766 lifecyclephase is implemented to perform cognitive learning operations, describedin greater detail herein. In certain embodiments, an inferred learningstyle, described in greater detail herein, is implemented by the CILS toperform these cognitive learning operations. In one embodiment, aconcept entailment cognitive learning technique is implemented by theCILS to perform a cognitive learning operation in the infer 766lifecycle phase. In another embodiment, a contextual recommendationcognitive learning technique is implemented by the CILS to perform acognitive learning operation in the infer 766 lifecycle phase.

In these embodiments, the CILS may implement a probabilistic reasoningmachine learning algorithm, described in greater detail herein, incombination with the concept entailment or contextual recommendationcognitive learning technique. In certain embodiments, the CILS mayimplement a reinforcement learning approach, likewise described ingreater detail herein, in combination with the concept entailment orcontextual recommendation cognitive learning technique. Skilledpractitioners of the art will recognize that many such embodiments arepossible. Accordingly, the foregoing is not intended to limit thespirit, scope or intent of the invention.

In various embodiments, the interpret 768 lifecycle phase is implementedto interpret the results of a cognitive learning operation such thatthey are consumable by a recipient, and by extension, present it in aform that is actionable in the act 770 lifecycle phase. In variousembodiments, the act 770 lifecycle phase is implemented to support aninteraction 750, described in greater detail herein. In certainembodiments, the interaction 750 includes interactions with a user,likewise described in greater detail herein. Skilled practitioners ofthe art will recognize that many such embodiments are possible.Accordingly, the foregoing is not intended to limit the spirit, scope orintent of the invention.

FIG. 8 is a simplified process flow diagram of cognitive machinelearning operations performed in accordance with an embodiment of theinvention to generate a hierarchical abstraction of topics within acorpus. In various embodiments, a cognitive inference and learningsystem (CILS) utilizes a cognitive machine learning algorithm, asdescribed in greater detail herein, to perform the cognitive machinelearning operations. In various embodiments, data curation 810operations are performed on a corpus, such as a product catalog, togenerate a product-by-feature matrix ‘Y’ 806 and aproduct-by-user-interaction matrix ‘B’ 808. As used herein, a featurebroadly refers to an attribute, and a user interaction broadly refers toany interaction a user may have with a given product. As an example, auser may select a particular product from three candidate products, eachof which has a different set of associated features, displayed on a webpage. In this example, the user interaction is the user's act ofselecting the chosen product according to its associated features.

In these embodiments, each row of the product-by-feature matrix ‘Y’ 806represents a particular product d and each column corresponds to aparticular feature v associated with that product d. Likewise, each rowof the user-interaction-by-product matrix ‘B’ 808 represents aparticular user u and each column corresponds to a particular product dassociated with that that user's interaction v. As an example, the firstrow of the product-by-feature matrix ‘Y’ 806 may include product d₁,which is associated with features v₅, v₇, v₁₀, and so forth. Likewise,the second row of the product-by-feature matrix ‘Y’ 806 may includeproduct d₂, which is associated with features v₆, v₇, v₁₁, and so forth.To continue the example, the first row of theuser-interaction-by-product matrix ‘B’ 808 may likewise include productd₁, which is associated with users u₁:1, u₂:0, u₃:0, u₄:1 and so forth.Likewise, the second row of the product-by-feature matrix ‘Y’ 806 mayinclude product d₂, which is associated with users u₁:0, u₂:0, u₃:1,u₄:0 and so forth. To further continue the example, the use of a ‘1’denotes a user's interaction with the product and the use of a ‘0’denotes the absence of a user's interaction.

As another example, a skillet may be a product that has a correspondingfeature of being “non-stick.” In certain embodiments, a feature has aone-to-one association with its corresponding product, yet differentproducts may have the same feature. To continue the example, theafore-mentioned skillet and a saucepan may be different products, yetboth may have the corresponding feature of being “non-stick.” Likewise,the skillet may have a feature of being eight inches in diameter whilethe saucepan may have a feature of ten inches in diameter. In furthercontinuance of the example, a user may perform a user interaction withthe skillet, such as selecting it as a candidate purchase on a web page,but not performing a similar user interaction with the saucepan.

In certain embodiments, the product-by-feature matrix ‘Y’ 806 may beimplemented as a document-by-word matrix. In these embodiments, each rowin the product-by-feature matrix ‘Y’ 806 represents a particulardocument and each word corresponds to a term associated with thatdocument. In various embodiments, term counts may be implemented todetermine their frequency in the document-by-word matrix. Those of skillin the art will recognize that many such embodiments and examples ofproducts, documents, features, terms, and user interactions arepossible. According, the foregoing is not intended to limit the spirit,scope or intent of the invention.

As shown in FIG. 8 , the dimensions of the product-by-feature matrix ‘Y’806 can be expressed as y_(dv) and the dimensions of theuser-interaction-by-feature matrix ‘B’ 808 can be expressed as b_(ud).Likewise, the indexing of the product-by-feature matrix ‘Y’ 806 can berepresented by y_(dw) where d is a particular product and v is aparticular feature. Likewise, the indexing of the product-by-userinteraction matrix ‘B’ 804 is represented by b_(ud) where u is aparticular user and d is a particular product.

In certain embodiments, the product-by-feature matrix ‘Y’ 806 may bedecomposed as the product of two matrices. The first matrix, referencedas θ, is a product-by-lowest-level-topics matrix 802. The second matrix,referenced as ϕ, is a feature-by-lowest-level-topics matrix 804. Inthese embodiments, the dimension of theta θ can be expressed as θ:dk₁and the dimension of ϕ can be expressed as ϕ:vk₁, where k₁ representsthe number of topics in the first level of a given hierarchy, which asdescribed in greater detail herein, is also its lowest level ofabstraction.

Accordingly, Y≈θ⁽¹⁾ϕ⁽¹⁾, B≈βΛϕ⁽¹⁾, and β:uK, where β denotes a user'sassociation with one or more types of users, such as various cognitivepersonas, described in greater detail herein. Likewise, Λ denotes themapping between various product clusters and various types of users, andK denotes the total number of unique users. By extension, an individualuser that is associated with a particular type of user is likely toprefer a certain group of products. As an example, an individual userwho is associated with a group of users who are machinists may have apreference for stainless steel machine screws. In contrast, anindividual user who is associated with a group of users who arewoodworkers may have a preference for brass wood screws. In thisexample, “stainless steel” and “machine” are features that machinistsprefer, while “brass” and “wood” are features that woodworkers prefer.

Once data curation 810 operations are completed, configurationparameters 812 are received, which are in turn used to perform topichierarchy configuration 814 operations. In various embodiments, theconfiguration parameters may include a target number of levels ofabstraction for a given hierarchy, along with a target number of nodesfor each level of the hierarchy. As an example, the hierarchy may bedefined as having 20 nodes in its lowest level, 10 in its second, 5 inits third, and 3 in its fourth. In this example, the dimension of θ forthe lowest level of abstraction is defined as θ⁽¹⁾:d 20, and thedimension of ϕ is defined as ϕ⁽¹⁾: 20 v, where ‘20’ is the number ofnodes in the lowest level of abstraction in the hierarchy. Likewise, thedimension of θ for the second lowest level of abstraction is defined asθ⁽²⁾:d 10, and the dimension of ϕ is defined as ϕ⁽²⁾: 10 v, where ‘10’is the number of nodes in the second lowest level of abstraction in thehierarchy. The process is then continued, level by level of thehierarchy, until the highest-defined level of abstraction, such as thefourth level, of the hierarchy is reached.

As used herein, a node broadly refers to either an individual topic or acluster of topics. As likewise used herein, a topic broadly refers to astatistical distribution of associated attributes. In certainembodiments, a topic may be a product, such as that found in a productcatalog, and its associated attributes may be features of the product.In various embodiments, the topic may be a document in a corpus ofcontent and its associated attributes may be terms it contains. Incertain embodiments, the affinity of individual attributes to a giventopic is defined by a probability vector with each feature representinga certain affinity score between ‘0’ and ‘1’. In various embodiments,each topic has an affinity towards a given node. However, in certain ofthese embodiments, this score is not bounded between ‘0’ and ‘1’.Instead, it can be any non-negative value.

As an example, a bolt and a machine screw may have a particular type ofthread that is different. Likewise, the bolt may have a hex head, whilethe machine screw may have a slotted head. As such, even though they mayhave features that are completely discrete from one another, they bothhave a strong affinity to the topic of “threaded fasteners.” However,both the bolt and machine screw may also have a “diameter” feature,which could be the same dimension. In this example, the feature of“diameter” could have an affinity to the topics of both “threadedfasteners,” such as bolts and machine screws, and “non-threadedfasteners,” such as cotter pins or pop rivets. Once topic hierarchyconfiguration 814 operations are completed, then core upward-downwardsampling operations, described in greater detail herein, are performed.

FIG. 9 shows the use of Gibbs sampling by a cognitive machine learningalgorithm implemented in accordance with an embodiment of the invention.In various embodiments, a generative model may have a plurality ofparameters, or variables, such as:

x₁, x₂ . . . x_(n)

where the joint distribution ‘P’ of the variables can be described as:

P(x₁, . . . , x_(n)).

Skilled practitioners of the art will be familiar with generativeprocesses, which are often used to generate a probabilistic model. Asused herein, a generative process broadlys refer to various approachesfor randomly generating observable data values, generally given somehidden parameters. As such, they typically specify a joint probabilitydistribution over observation and label sequences. Those of skill in theart will also be aware that generative models are commonly used inmachine learning for either modeling data directly, such as modelingobservations drawn from a probability density function, or as anintermediate step to forming a conditional probability density function.

Skilled practitioners of the art will likewise be aware that aconditional distribution can be formed from a generative model throughBayes' rule, which is commonly used to generate a Bayesian network. Asused herein, a Bayesian network broadly refers to a probabilisticgraphical model that represents a set of random variables and theirconditional dependencies via a directed acyclic graph (DAG). Moreparticularly, Bayesian networks are DAGs whose nodes represent randomvariables in that they may be observable quantities, latent variables,unknown parameters, or hypotheses. Likewise, edges represent conditionaldependencies and nodes that are not connected represent variables thatare conditionally independent of one another. In such approaches, eachnode is associated with a probability function that takes, as input, aparticular set of values for the node's parent variables. Each such nodealso gives, as output, the probability distribution of the variablerepresented by the node.

As likewise used herein, a probabilistic model broadly refers to aknowledge graph construction resulting from the extraction ofinformation from a knowledge population, and the inference of missinginformation. In various probabilistic modeling approaches, the missinginformation is inferred through a statistical analysis of the extractedinformation. It will be appreciated that it is not uncommon forinsufficient external resources in the knowledge population to hindersuch statistical inference. In various embodiments, gaps between thesetwo processes may be reduced by an incremental population approach thatbenefits from the path structure of existing knowledge.

In certain embodiments, the joint distribution P(x₁, . . . , x_(n)) ofthese variables may not initially be known, yet it is desirable todetermine the particular values of x₁, x₂ . . . x_(n) that will maximizethe joint probability P( ). Accordingly, Gibbs sampling approaches knownto those of skill in the art are implemented in various embodiments toperform sampling operations. Skilled practitioners of the art will befamiliar with a Gibbs sampler, which is a Markov chain Monte Carlo(MCMC) algorithm for obtaining a sequence of observations that areapproximated from a specified multivariate probability distribution. Inparticular, Gibbs sampling is often utilized when direct sampling isdifficult. The sequence of observations resulting from Gibbs samplingcan be used for a variety of purposes. For example, the sequence may beused to approximate joint distribution, such as when generating ahistogram of the distribution. The sequence can likewise be used toapproximate the marginal distribution of a particular variable, or somesubset of variables, such as unknown parameters or latent variables.Likewise, the sequence may be used to compute an integral, such as theexpected value of one of the variables. In certain embodiments, some ofthe variables may correspond to observations whose values are known andtherefore do not require sampling.

Those of skill in the art will likewise be familiar with Markov chains,which in probability theory and statistics are a stochastic process thatsatisfies the Markov property, which is typically characterized as“memorylessness.” In general, a process satisfies the Markov property ifthe future outcome of the process can be predicted solely upon itspresent state, and likewise, the same results can be realized as if thecomplete history of the process was known. In particular, conditionalupon the present state of a given system, its future and past areindependent in a Markov process.

More particularly, when implemented in discrete time, a Markov processis known as a discrete-time Markov chain (DTMC). Such processes undergotransitions from one state to another for a given state space, with theprobability distribution of the next state dependent only upon thecurrent state and not on the sequence of events that preceded it.Likewise, in continuous time, such processes are known as acontinuous-time Markov chain (CTMC), or alternatively, as acontinuous-time Markov process. These processes take values in a givenfinite state space, with the time spent in each state takingnon-negative real values and having a corresponding exponentialdistribution. Accordingly, future behavior of the model, for both thetime remaining in the current state and the next state, depends onlyupon the current state of the model and not on historical behavior.

In these embodiments, a Gibbs sampling operation is performed todetermine the probability of x₁ as follows:x ₁ ˜P(x ₁| . . . )902,such that the value of x is fixed at a particular value.The resulting value of x₁ is then used to determine the conditionaldistribution of x₂ by sampling x₂ as follows:x ₂ ˜P(x ₂ |x ₁ . . . )904.In turn, the resulting value of x₂ is then used to determine theconditional distribution of x₃ by sampling x₃ as follows:x ₃ ˜P(x ₂ |x ₁ ,x ₂ . . . )906The procedure is then repeated to determine the conditional distributionof x_(n) by sampling x_(a) as follows:x _(n) ˜P(x _(n) |x ₁ ,x ₂ ,x ₃ . . . )908

Upon completion of sampling x_(n) the process continues as a samplingcycle 910. In various embodiments, the number of times the samplingcycle 910 is performed is a matter of design choice. In certainembodiments, the resulting sampling values of x₁, x₂ . . . x_(a) fromeach sampling cycle are respectively averaged, which in turn results inmaximizing the joint probability P( ).

FIG. 10 depicts upward-downward sampling operations performed by acognitive machine learning algorithm implemented in accordance with anembodiment of the invention. In various embodiments, the cognitivemachine learning algorithm may be implemented as an augmented GammaBelief Network (GBN) 1000. Skilled practitioners of the art will befamiliar with various GBN 1000 approaches, which are often implementedto infer a multi-level representation of high-dimensional discrete ornon-negative real vectors. In certain embodiments, the augmented GBN1000 may be implemented to factorize each of its hidden hierarchy levelsT 1002 of abstraction into the product of a sparse connection weightmatrix and the nonnegative real hidden units of the next hierarchy levelt¹ 1004 through t^(n) 1006 of abstraction. In various embodiments, theaugmented GBN 1000 may be implemented as a domain topic abstractionalgorithm, a hierarchical topic navigation algorithm, a temporal topicdiscovery algorithm, or some combination thereof.

In various embodiments, individual nodes in the augmented GBN 1000 areimplemented as clusters of domain topics in a hierarchical topic model,described in greater detail herein. In certain embodiments, the domaintopics are characterized as products, such as products in a productcatalog, and the data attributes associated with each product arecharacterized as product features. In various embodiments, individualnodes in the augmented GBN 1000 are implemented as clusters of eventtopics in a temporal topic model, likewise described in greater detailherein. In certain embodiments, the event topics are characterized asportions of corpora associated with a particular temporal event andtheir associated data attributes are characterized as terms.

In various embodiments, a product-by-feature matrix M 1008, described ingreater detail herein, is the product of two sub-matrices, aproduct-by-topic matrix and a topic-by-feature matrix, likewisedescribed in greater detail herein. These two sub-matrices, representedby variables 1010 θ and ϕ, are Poisson 1022 distributed as follows:M≈Pois(θ⁽¹⁾ϕ⁽¹⁾)Skilled practitioners of the art will be familiar with Poisson 1022distribution, which is a discrete probability distribution. In variousknown approaches, Poisson 1022 distributions express the probability ofa given number of events occurring in a fixed interval of time, space,or a combination thereof. Poisson 1022 distributions likewise assumethat such events occur independently of each preceding event with aknown average rate.

In these embodiments, it is desirable to have values greater than 0 forθ⁽¹⁾ 1012 and ϕ⁽¹⁾ 1014, as negative values for θ⁽¹⁾ 1012 and ϕ⁽¹⁾ 1014would imply a negative affinity to a given topic. However, a valuegreater than or equal to 0 for either θ⁽¹⁾ 1012 or ϕ⁽¹⁾ 1014 wouldrespectively imply θ⁽¹⁾ 1012 or ϕ⁽¹⁾ 1014 having either a positiveaffinity, or no affinity, for a given topic. Accordingly, a gamma 1024distribution is imposed as a prior over θ⁽¹⁾ 1012 and a Dirichlet 1026distribution is imposed as a prior over ϕ⁽¹⁾ 1014.

Those of skill in the art will be familiar with gamma 1024 distribution,which in the fields of probability theory and statistics refers to atwo-parameter family of continuous probability distributions 1020. Ingeneral, three different parametrizations are commonly used:

-   -   a shape parameter k and a scale parameter θ    -   a shape parameter α=k and an inverse scale parameter β=1/θ,        known as a rate parameter, and    -   a shape parameter k and a mean parameter ρ=k/β        In each of these three forms, both parameters are positive real        numbers. Skilled practitioners of the art will likewise be        familiar with Dirichlet distributions, often denoted Dir (α),        which in the fields of probability and statistics refers to a        family of continuous multivariate probability distributions        parameterized by a vector of positive reals. As such, it is a        multivariate generalization of the beta distribution.

Accordingly, the gamma 1024 and Dirichlet 1026 distribution is imposedsuch that:θ⁽¹⁾˜Gamma( ), andϕ⁽¹⁾˜Dir( ),which in certain embodiments results in the determination of conditionaldistributions 1020 as follows:θ⁽¹⁾|˜ andϕ⁽¹⁾|˜and so forth, for each associated level t¹ 1004 through t^(n) 1006 ofthe augmented GBN 1000 hierarchy.

In various embodiments, once conditional distributions 1020 have beendetermined for each node in the augmented GBN 1000, then augmentationvariables 1028 of L¹ 1030 through L^(n) 1032 are respectively applied toeach corresponding node θ⁽¹⁾ 1012 through θ^((n)) 1016. In theseembodiments, the application of the augmentation variables 1028 of L¹1030 through L^(n) 1032 are used in the performance of downward sampling1034 operations, beginning with level t^(n) 1006 and proceeding,level-by-level, to level t¹ 1004, as described in greater detail herein.In certain embodiments, the application of the augmentation variables1028 of L¹ 1030 through L^(n) 1032 result in a tractable form of theconditional distribution 1020 associated with each correspondingvariable θ⁽¹⁾ 1012 through θ^((n)) 1016, which allows sampling of θ⁽¹⁾1012 and ϕ⁽¹⁾ 1014 to be performed more easily. In various embodiments,the augmentation variables 1028 of L¹ 1030 through L^(n) 1032 areapplied according to a Poisson 1022 distribution approach.

In various embodiments, augmentation of a GBN 1000 is based upon theassertion:a x _(k)˜Pois(ζ_(k))∀k, and:X=Σ _(k=1) ^(K) x _(k),ζ=Σ_(t=1) ^(K)ζ_(k), and if:(y ₁ , . . . ,y _(K))˜Mult(X;ζ ₁/ζ, . . . ,ζ_(K)/ζ), then:P(x ₁ , . . . ,x _(K))=P(y ₁ , . . . ,y _(k) ;X)

Accordingly, the negative binomial (NB) distribution m˜NB(r, p), incombination with the probability mass function (PMF):

${{\Pr\left( {M = m} \right)} = {{\frac{{Gam}\left( {m + r} \right)}{{m!}{{Gam}(r)}}{p^{m}\left( {1 - p} \right)}^{r}{for}m} \in Z}},$can be augmented into a gamma 1024-Poisson 1022 construction as:m˜Pois(Δ),λ˜Gam(r,p/(1−p)),where the gamma 1024 distribution is parameterized by its shape r andscale p/(1−p). Likewise, it can also be augmented under a compoundPoisson 1022 representation as:

${m = {\sum\limits_{t = 1}^{l}u_{t}}},{u_{t}\overset{iid}{\sim}{{Log}(p)}},{l \sim {{Pois}\left( {{- r}{\ln\left( {1 - p} \right)}} \right)}},$

u˜Log(p) is the logarithmic distribution.

In certain embodiments, the augmented GBN's 1000 hierarchy levels 1002are jointly trained with a Gibbs sampler implemented to perform upward1024 and downward 1034 sampling. In various embodiments, each upward1024 sampling iteration of the augmented GBN 1000 propagates latentcounts and samples Dirichlet 1026 distributed connection weight vectorsstarting from a bottom-most level t¹ 1004.

In certain embodiments, downward 1034 sampling of the augmented GBN 1000iteratively samples gamma 1024 distributed hidden units starting fromthe top hierarchy level t^(n) 1006, with each lower level (e.g., levelt³) solved with the same sequence of operations. In these embodiments,the gamma-negative binomial process, combined with such a level-wisetraining approach, allows the augmented GBN 1000 to infer the width ofeach hierarchy level 1002 given a fixed budget on the width of thefirst, or bottom-most level t¹ 1004.

In certain of these embodiments, the basis for the probabilistic modelis established by first implementing a product-by-feature matrixY∈0,1^(D×V), described in greater detail herein, wherein the (d, w)^(th)entry of the matrix is denoted by y_(dw) and is linked to a latent countas follows:

${x_{dw}^{(1)} \sim {{{Pois}\left( {\sum\limits_{k_{1} = 1}^{K_{1}}{\theta_{{dk}_{1}}^{(1)}\phi_{{wk}_{1}}^{(1)}}} \right)}{as}y_{dw}}} = I_{\{{x_{dw}^{(1)} > 0}\}}$

In these embodiments, a tree-structured prior over θ_(dk) ₁ ⁽¹⁾'s ismaintained, wherein the hierarchy tree has a depth 1002 of T, the leavesare represented by the θ_(dk) ₁ ⁽¹⁾'s and the top level nodes, such asthe nodes in hierarchy level t^(n) 1024, are represented by θ_(dk) _(T)^((T))'s, with K_(t) being the greatest number of nodes at a givenhierarchy level 1002 T^(th). Accordingly, the top-most hierarchy level1002 T^(th) of the tree is sampled as:φ_(k) _(T) ^((T))˜Dir(η_(T))∀k _(T)∈{1, . . . ,K _(T)}, as well as:aθ _(dk) _(T) ^((T)) ˜Gam(r _(k) _(T) ,l/c _(d) ^((T)))∀k _(T)∈{1, . . .,K _(T)} for ∀k _(T) ,d

To complete the generative process, priors are imposed over r_(k) _(T)as:r _(k) _(T) ˜Gam(γ₀ /K _(T),1/c ₀)∀k _(T)∈{1, . . . ,K _(T)}, where:γ₀˜Gam(a ₀,1/b ₀),c ₀˜Gam(e ₀,1/f ₀), and φ_(K) _(T) ^((T)) 's are thensampled from Dirichlet distribution. Consequently:

${{\sum\limits_{k_{({T - 1})} = 1}^{K_{({T - 1})}}\phi_{k_{({T - 1})}k_{T}}^{(T)}} = {1{\forall{{k_{T}{and}\phi_{k_{({T - 1})}k_{T}}^{(T)}} \geq {0{\forall k_{({T - 1})}}}}}}},k_{T}$

Likewise, each T^(th) level 1002 in the hierarchy tree, where 1≤t<T1026, is sampled as:φ_(k) _(t) ^((t))˜Dir(η^((t)) ∀k _(t)∈{1,2, . . . ,K ^(t),}, followedby:

${{\sum\limits_{k_{({t - 1})} = 1}^{K_{({t - 1})}}\phi_{k_{({t - 1})}k_{t}}^{(t)}} = {1{\forall k_{t}}}},{\phi_{k_{({t - 1})}k_{t}}^{(t)} \geq {0{\forall k_{({t - 1})}}}},k_{t},$and:

∀k_(t),d, is sampled as:

$\theta_{{dk}_{t}}^{(t)} \sim {{Gam}\left( {{\sum\limits_{k_{({t + 1})} = 1}^{K_{({t + 1})}}{\theta_{{dk}_{({t + 1})}}^{({t + 1})}\phi_{k_{t}k_{({t + 1})}}^{({t + 1})}}},{1/c_{d}^{(t)}}} \right)}$

In certain embodiments, K₁=V is maintained, such that bottom-leveltopics, such as those topics associated with hierarchy level t¹ 1004,are connected to all features, resulting in any topic at any hierarchylevel 1002 t>1 having indirect connection to all features. Likewise, thevariability in the number of features each individual product of theaugmented GBN 1000 can possess is accounted for by:c _(d) ^((t))˜Gam(e ₀,1/f ₀)∀d

In various embodiments, this generative process induces a hierarchicalfactorization of the features in the product-by-feature matrix M 1008.Consequently, this generative process can be further augmented with theuser-by-product interaction matrix B, which is a binary matrix ofdimension U×D that encodes the “likes” and “dislikes” of the userscorresponding to each of the products in the matrix M 1008. In theseembodiments, the (u,d)^(th) entry of the matrix M 1008 is denoted byB_(ud), wherein.B _(ud)˜Pois₊(β_(u)Λθ_(d) ⁽¹⁾

).

Likewise, β_(u)∈R₊ ^(K) is the representation of the u^(th) user in thelatent space spanned by all different users and is sampled as β_(u)˜Dir(ζ)∀u, with the existence of at most K different users. For a givend, θ_(d) ⁽¹⁾ 1004 likewise represents the product from the lowest levelof the hierarchical factorization, such as hierarchy level t¹ 1004, toinfluence a user's decision to “like” a product. Likewise, Λ∈R₊ ^(K×K) ¹models the association between the set of user factors K and productfactors K₁, and can be sampled as:Λ_(k,k) _(1˜Gam(ρ) ₀ /K,1/c)Consequently, following the properties of Poisson 1014 distribution:

${B_{ud} = I_{\{{b_{ud} > 0}\}}},{b_{ud} = {\sum\limits_{k_{1},\overset{\_}{k}}b_{{ud}\overset{\_}{k}k_{1}}}},{b_{{ud}\overset{\_}{k}k_{1}} \sim {{{Pois}\left( {\beta_{u\overset{\_}{k}}\Lambda_{\overset{\_}{k},k_{1}}\theta_{{dk}_{1}}^{(1)}} \right)}.}}$

From the foregoing, skilled practitioners of the art will recognize thatthe described augmentation of the interaction matrix ensures that thehierarchical construction of the augmented GBN 1000 is modified suchthat it reflects a user's preferences. Furthermore, the foregoingprovides an approach for unsupervised generation of a hierarchicalabstraction of a corpus of content in the absence of user-productinteraction data. However, it will be appreciated that the automatedcategorization of products within such a hierarchy may not matchsemantic concepts typically perceived by users.

Accordingly, there may be a need to reorganize the hierarchy of theaugmented GBN with user-product interaction data. In variousembodiments, feedback from the user, or updates to the hierarchicalfactorization, is not respectively received or performed in real time.Instead, feedback is collected from users for a certain period of time,such as a day or two, followed by using the collected interaction datato re-learn the representation pf the augmented GBN 1000. In certainembodiments, such hierarchical categorization of features facilitatesthe discovery of abstract semantic correlation, which is useful foridentifying a user's explicit preferences over a collection of implicitfeatures within the GBN 1000.

In various embodiments, the network structure of the augmented GBN 1000can be expressed as:

${{{E\left\lbrack {{\left\lbrack {x_{d}^{(1)},b_{\cdot d}} \right\rbrack ❘\theta_{d}^{(t)}},\left\{ {\Phi^{(\ell)},c_{d}^{(\ell)}} \right\}_{1,t}} \right\rbrack} = {{\left\lbrack {\Phi^{(1)},\Lambda} \right\rbrack\left\lbrack {\prod\limits_{\ell = 2}^{t}\Phi^{(\ell)}} \right\rbrack}\frac{\theta_{d}^{(t)}}{\prod\limits_{\ell = 2}^{t}c_{d}^{(\ell)}}}},{{and}:}}{{E\left\lbrack {{\theta_{d}^{(t)}❘\left\{ {\Phi^{(\ell)},c_{d}^{(\ell)}} \right\}_{{t + 1},T}},r} \right\rbrack} = {\left\lbrack {\prod\limits_{\ell = {t + 1}}^{T}\Phi^{(\ell)}} \right\rbrack\frac{r}{\prod\limits_{\ell = {t + 1}}^{T + 1}c_{d}^{(\ell)}}}}$wherein “[. . . ]” denotes concatenation of row vectors and matrices.For visualization, the K_(t) topics of hierarchy level 1002 t∈{1, . . ., T} can likewise be projected to the bottom data hierarchy level t¹1004 for visualization as the columns of the (V+K)×K, matrix [Φ⁽¹⁾,∀]Π_({dot over (t)}=2) ^(l)Φ^(({dot over (t)})), with their respectivepopularity being ranked using the K_(t) dimensional non-negative weightvector r^((t)):=└Π_({dot over (t)}=(t+1))Φ^(({dot over (t)}))┘r.

In certain embodiments, the value of ϕ_(k′,k) ^((t)) can be used tomeasure the connection strength between a particular node k of ahierarchy level 1002 t and a node k′ of a hierarchy level 1002 (t−1).Accordingly, skilled practitioners of the art will recognize that thedifferences between typical and augmented GBN 1000 approaches can beobserved in these derivations. It can likewise be observed that variousabstract user groups, such as users sharing a common cognitive persona,can act as implicit features of dimension K along with the explicitproduct features of dimension V. Likewise, φ_(k) _(t) ^((t))'s at eachlevel 1002 of the hierarchy represent a certain combination of featuresthat can be considered a signature of their corresponding node. As anexample, the hierarchy may represent a collection of women's dresses.Accordingly, a given φ_(k) _(t) ^((t)) might represent “50% animalprint, 40% pink, 5% party dress,” and “5% of urban casual fashionbuyers.”

From the foregoing, those of skill in the art will recognize that thesepercentages can be calculated from the posterior assignment of eachφ_(k) _(t) ^((t)), which by construction, is a probability vector. Insuch cases, the corresponding θ_(dk) _(t) ^((t)) represents the affinityof the d^(th) product towards these attributes. Accordingly, if theu^(th) user happens to prefer a particular product, then her associatedcognitive persona is more likely to be described as “50% animal print,40% pink, 5% party dress,” and “5% of urban casual fashion buyer” andbelong to this category. Consequently, user types can be personified incertain embodiments as abstract groups that emerge from the interactionpattern.

In various embodiments, closed-form updates for Gibbs sampling of alllatent variables in a model can be derived such that inference is fast,comprehensive, and scalable when processing corpora of content thatinclude large amounts of associated data. As an example, x_(dw) ⁽¹⁾ onlyneeds to be sampled when y_(dw)>0, which can be described as:

${x_{dw}^{(1)}❘{\ldots \sim {y_{dw}{{Pois}_{+}\left( {\sum\limits_{k_{1} = 1}^{K_{1}}{\theta_{{dk}_{1}}^{(1)}\phi_{{wk}_{1}}^{(1)}}} \right)}}}},$and accordingly, sampling of x_(dwk) _(l) ⁽¹⁾ can be described as:

$\left( x_{{dwk}_{1}}^{(1)} \right)_{k_{1} = 1}^{K_{1}}❘{\ldots \sim {{mult}\left( {\left( {\theta_{{dk}_{1}}^{(1)}\phi_{{wk}_{1}}^{(1)}/{\sum\limits_{k_{1} = 1}^{K_{1}}{\phi_{{dk}_{1}}^{(1)}\phi_{{wk}_{1}}^{(1)}}}} \right)_{k_{1} = 1}^{K_{1}};x_{dw}^{(1)}} \right)}}$

Likewise,

$\left( b_{{ud}\overset{\_}{k}k_{1}} \right)_{\overset{\_}{k}k_{1}}$only needs to be sampled in certain embodiments when B_(ud)>0. In theseembodiments, the sampling operations can be described as:

$\left( b_{{udkk}_{1}} \right)_{{kk}_{1}}❘{\ldots \sim {B_{ud}{{Pois}_{+}\left( {\sum\limits_{\overset{\_}{k},k_{1}}{\beta_{u\overset{\_}{k}}\Lambda_{{kk}_{1}}\theta_{{dk}_{1}}^{(1)}}} \right)}}}$

In various embodiments, upward 1006 sampling of θ_(dk) ₁ ^((t)) 1004 forhierarchy levels t¹ 1022 to t^(n) 1024 can likewise be based upon theassertion that if:λ˜Gam(r,1/c),x _(t)˜Poisson(m _(i)λ), then:λ|{x _(i)}˜Gam(r+Σ _(i) x ₁,1/(c+Σ _(i) m _(i))),which in turn can be described as:

$\theta_{{dk}_{1}}^{(1)}❘{\ldots \sim {{Gam}\left( {{{\sum\limits_{k_{2} = 1}^{K_{2}}{\theta_{{dk}_{2}}^{(2)}\phi_{k_{1}k_{2}}^{(2)}}} + x_{d \cdot k_{1}}^{(1)} + b_{\cdot d \cdot k_{1}}},{1/\left( {c_{d}^{(1)} + 1 + \Lambda_{\cdot k_{1}}} \right)}} \right)}}$

From the foregoing, those of skill in the art will recognize how thesampling of the θ⁽¹⁾'s is affected by the observation matrix B. Inparticular, as the number of user interactions increase, they have acorresponding effect on the posterior of the θ₍₁₎'s 1004. Consequently,the hierarchical construction of the features is shaped by each userinteraction ‘i’ as more data is gathered. Accordingly, for i=2ωi=T 1026,augmentation operations are implemented with the GBN 1000 in variousembodiments as follows:

x_(dk_((t − 1)))^((t))❘… ∼ CRT(x_(dk_((t − 1)))^((t − 1)), ⟨θ_(d)^((t)), φ_(k_((t − 1)))^((t))⟩)to determine which data x is transferred from a lower hierarchy level1002 t (e.g., t²), in the augmented GBN 1000 to a higher hierarchy level1002 t (e.g., t³), where d represents a product in the augmented GBN1000 and k represents a particular node.

Thereafter, the following multinomial sampling can be used in variousembodiments to split the augmented count amongst the topics at aparticular hierarchy level 1002 t:

$\left( x_{{dk}_{({t - 1})}k_{t}} \right)_{k_{t} = 1}^{K_{t}}❘{\ldots \sim {{mult}\left( {x_{{dk}_{({t - 1})}}^{(t)};\left( {\theta_{{dk}_{t}}^{(t)}\phi_{k_{({t - 1})}k_{t}}^{(t)}/{\sum\limits_{k_{t} = 1}^{K_{t}}{\theta_{{dk}_{t}}^{(t)}\phi_{k_{({t - 1})}k_{t}}^{(t)}}}} \right)_{k_{t} = 1}^{K_{t}}} \right)}}$

Accordingly, assuming m˜NB(r, p) is represented under its compoundPoisson 1022 representation, then the conditional posterior of l given mand r has PMF as follows:

${{\Pr\left( {{l = {j❘m}},r} \right)} = {\frac{\Gamma(r)}{\Gamma\left( {m + r} \right)}{❘{s\left( {m,j} \right)}❘}r^{j}}},{j = 0},1,\ldots,m$where |s(m, j)| are unsigned Stirling numbers of the first kind.Accordingly, this conditional posterior can then be denoted as l˜CRT(m,r), a Chinese restaurant table (CRT) count random variable, which inturn can be generated via:l=Σ _(n=1) ^(m) z _(n) ,z _(n)˜Bernoulli(r/(n−1+r))Using this approach, the conditional posterior for θ_(dk) _(t) ^((t))can then be derived as:

$\left. {{\theta_{{dk}_{t}}^{(t)}❘{\ldots \sim {{{Gam}\left( {\sum\limits_{k_{({t + 1})}}{\theta_{{dk}_{({t + 1})}}^{({t + 1})}\phi_{k_{t}k_{({t + 1})}}^{({t + 1})}}} \right\rangle} + x_{d.k_{t}}}}},{1/\left( {c_{d}^{(1)} + 1} \right)}} \right)$

In certain embodiments, downward 1034 sampling of θ_(dk) _(t) ^((t)) forhierarchy levels t^(n) 1032 to t¹ 1030 can once again be based upon theassertion that if m˜ NB(r, p) is represented under its compound Poisson1022 representation, then the conditional posterior of l given m and rhas PMF as follows:

${{\Pr\left( {{l = {j❘m}},r} \right)} = {\frac{\Gamma(r)}{\Gamma\left( {m + r} \right)}{❘{s\left( {m,j} \right)}❘}r^{j}}},{j = 0},1,\ldots,m$where |s(m, j)| are unsigned Stirling numbers of the first kind.Accordingly, this conditional posterior can then be denoted asl˜CRT(m,r), which once again can be generated via:l=Σ _(n=1) ^(m) z _(n) ,z _(n)˜Bernoulli(r/(n−1+r))Using this approach, the conditional posterior for θ_(dk) _(t) ^((t))can then likewise be derived once again as:

$\left. {{\theta_{{dk}_{t}}^{(t)}❘{\ldots \sim {{{Gam}\left( {\sum\limits_{k_{({t + 1})}}{\theta_{{dk}_{({t + 1})}}^{({t + 1})}\phi_{k_{t}k_{({t + 1})}}^{({t + 1})}}} \right\rangle} + x_{d \cdot k_{t}}}}},{1/\left( {c_{d}^{(1)} + 1} \right)}} \right)$

In various embodiments, downward 1034 sampling of φ_(k) _(t) ⁽¹⁾ forhierarchy levels t^(n) 1032 to t¹ 1030 can be based upon the assertionthat if:x _(w)˜Pois(mβ _(w))∀w∈{1,2, . . . ,V}, andβ˜Dir(η), then:β|˜Dir(η₁ +x ₁, . . . ,η_(V) +x _(V)), and accordingly:

φ_(k_(t))^((t))❘… ∼ Dir(η₁^((t)) + x_(⋅1k_(t))^((t)), ⋯, η_(K_(t))^((t)) + x_(⋅K_((t − 1))k_(t))^((t))), ∀2 ≤ t ≤ T

In certain embodiments, downward 1034 sampling of c_(d) ^((t)) forhierarchy levels l_(n) 1032 to t¹ 1030 can be based upon the assertionthat if:r _(i)˜Gam(a _(i),1/b)∀i∈{1,2, . . . ,K},b˜Gam(c,1/d), then:b|{r _(i)}˜Gam(Σ_(n=1) ^(K) a _(t) +c,1/(Σ_(i=1) ^(K) r _(i) +d), andaccordingly:

${c_{d}^{(t)}❘{\ldots \sim {{{Gam}\left( {{e_{0} + {\sum\limits_{k_{t} = 1}^{K_{t}}\theta_{{dk}_{({t + 1})}}^{({t + 1})}}},{1/\left( {f_{0} + {\sum\limits_{k_{t} = 1}^{K_{t}}\theta_{{dk}_{t}}^{(t)}}} \right)}} \right)}{\forall d}}}},{1 \leq t < T}$

In various embodiments, other variables that may not be directly relatedto particular nodes within the augmented GBN 1000 may be sampled usingvarious approaches. In one embodiment, when B_(ud)>0, the variableb_(wdk,k) _(j) can be sampled as follows:

$\left( b_{{udk},k_{1}} \right)_{{kk}_{1}}❘{\ldots \sim {B_{ud}{{Pois}_{+}\left( {\sum\limits_{k,k_{1}}{\beta_{uk}\Lambda_{{kk}_{1}}\theta_{{dk}_{1}}^{(1)}}} \right)}}}$

In another embodiment, sampling of the variable β_(k) can be based uponthe assertion that if:λ˜Gam(r,1/c),x _(i)˜Poisson(m,λ), then:λ|{x _(i)}˜Gam(r+Σ _(i) x _(i),1/(c+Σ _(i) m _(i))), and consequently:β_(k) ⁻ | . . . ˜Dir(ζ+b _(1k . . .) ,ζ+b _(2k . . .) , . . . ,ζ+b _(Uk). . . )

In yet another embodiment, the variable r_(k) _(T) can be sampled byfirst performing augmentation operations as follows:a x _(dk) _(T) ^((T+1)) | . . . ˜CRT(x _(dk) _(T) ^((T)) ,r _(K) _(T) )and as before, based upon the assertion that if m˜NB(r, p) isrepresented under its compound Poisson 1022 representation, then theconditional posterior of l given m and r has PMF as follows:

${{\Pr\left( {{l = {j❘m}},r} \right)} = {\frac{\Gamma(r)}{\Gamma\left( {m + r} \right)}{❘{s\left( {m,j} \right)}❘}r^{j}}},$j=0, 1, . . . , mwhere |s(m, j)| are unsigned Stirling numbers of the first kind.Accordingly, this conditional posterior can then be denoted as l˜CRT(m,r), which once again can be generated via:l=Σ _(m=1) ^(m) z _(n) ,z _(n)˜Bernoulli(r/(n−1+r))thereby allowing r_(k) _(T) to be sampled as follows:

$r_{k_{T}}❘{\ldots \sim {{Gam}\left( {{{\gamma_{0}/K_{T}} + {\sum\limits_{d}x_{{dk}_{T}}^{({T + 1})}}},{1/\left( {c - {\sum\limits_{d}{\log\left( {1 - p_{d}^{(T)}} \right)}}} \right)}} \right)}}$

In yet still another embodiment, the sampling of variable A, can bebased upon the assertion that if:λ˜Gam(r,1/c),x _(i)˜Poisson(m,Δ), then:λ|{x _(i)}˜Gam(r+Σ _(i) x _(i),1/(c+Σ _(i) m _(i))),thereby allowing the sampling of Λ_(kk) ₁ to be performed as follows:Λ _(kk) ₁ | . . . ˜Gam(ρ₀ /K+b _(..kk) ₁ )/(b ₀+θ_(k) ₁ ))

In one embodiment the variable c₀ can be sampled based upon theassertion that if:r ^(i)˜Gam(a _(i),1/b)∀i∈{1,2, . . . ,K},b˜Gam(c,1/d), then:b|{r _(i)}˜Gam(Σ_(n=1) ^(K) a _(i) +c,1/(Σ_(n=1) ^(K) r _(i) +d),thereby allowing c₀ to be sampled as:

$c_{0}❘{\ldots \sim {{Gam}\left( {{e_{0} + \gamma_{0}},{1/\left( {f_{0} + {\sum\limits_{k_{T} = 1}^{K_{T}}r_{k_{T}}}} \right)}} \right)}}$

In another embodiment the variable γ₀ can be sampled by first performingaugmentation operations as follows:

$x_{k_{T}}^{({T + 2})}❘{\ldots \sim {{CRT}\left( {{\sum\limits_{d}x_{{dk}_{T}}^{({T + 1})}},{\gamma_{0}/K_{T}}} \right)}}$and as before, based upon the assertion that if m˜NB(r, p) isrepresented under its compound Poisson 1014 representation, then theconditional posterior of l given m and r has PMF as follows:

${{\Pr\left( {{l = {j❘m}},r} \right)} = {\frac{\Gamma(r)}{\Gamma\left( {m + r} \right)}{❘{s\left( {m,j} \right)}❘}r^{j}}},{j = 0},1,\ldots,m$where |s(m, j)| are unsigned Stirling numbers of the first kind.Accordingly, this conditional posterior can then be denoted as l˜CRT(m,r), which once again can be generated via:l=Σ _(m=1) ^(m) z _(n) ,z _(n)˜Bernoulli(r(n−1+r)),thereby allowing γ₀ to be sampled as follows:

$\gamma_{0}❘{\ldots \sim {{Gam}\left( {{a_{0} + {\sum\limits_{k_{T}}x_{k_{T}}^{({T + 2})}}},{1/\left( {b_{0} - {\frac{1}{K_{T}}{\sum\limits_{k_{T}}{\log\left( {1 - p_{k_{T}}^{({T + 1})}} \right)}}}} \right)}} \right)}}$

Those of skill in the art will recognize that many such embodiments andexamples are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

FIG. 11 is a simplified block diagram of cognitive machine learningoperations performed in accordance with an embodiment of the inventionto generate a hierarchical abstraction of topics within a corpus. Invarious embodiments, a cognitive inference and learning system (CILS)utilizes a cognitive machine learning algorithm, as described in greaterdetail herein, to perform the cognitive machine learning operations. Incertain embodiments, the cognitive machine learning algorithm may beimplemented as a domain topic abstraction algorithm. In theseembodiments, the hierarchical abstraction of topics 1116 is generated byapplying the domain topic abstraction algorithm to a corpus of contentassociated with a particular domain of information.

In various embodiments, the hierarchical abstraction of topics 1116 isgenerated in the form of a hierarchical topic model 1100. In certainembodiments, the hierarchical topic model 1100 is implemented in theform of an abstraction hierarchy, described in greater detail herein. Invarious embodiments, the corpus of content may include a collection ofdocuments, a text, an image, an audio recording, a video recording,streaming media (e.g., newsfeeds), a social media post, databaseelements, various kinds of metadata associated with the foregoing, orsome combination thereof.

As used herein, a topic broadly refers to a statistical distribution ofassociated attributes. In certain embodiments, a topic may be a product,such as that found in a product catalog, and its associated attributesmay be features of the product. In various embodiments, the topic may bea document in a corpus of content and its associated attributes may bethe terms it contains. In certain embodiments, a topic may also broadlyrefer to something that is being discussed, or illustrated, within agiven subset of a corpus, such as a phrase, an image, or a sound. Inthese embodiments, the difference between a topic and a grammaticalsubject is that a topic is used to describe the information structure ofa clause and how it coheres with other clauses, whereas a subject is agrammatical category. Furthermore, both a topic and a grammaticalsubject are distinguished from an actor, or agent, which is the “doer”within a particular subset of a corpus. Moreover, while a topic istypically the subject, the agent may be omitted or follow thepreposition “by” in English clauses with a verb in the passive voice.

As likewise used herein, an algorithm broadly refers to a predeterminedset of rules for conducting computational steps that produce acomputational effect. Likewise, as used herein, a programming model(“model”) broadly refers to a framework for expressing algorithms, butis not an algorithm itself. For example, an algorithm may provide apredetermined set of rules, or computational operations, for carryingout the steps to produce an outcome. In contrast, a programming modeldoes not describe how to carry out steps to solve an actual problem.Instead, it provides a framework for expressing algorithms to do so.

A topic model, as used herein, broadly refers to a statistical modelimplemented to discover abstract topics occurring within a corpus.Skilled practitioners of the art will be familiar with various topicmodeling approaches, which are frequently used in text mining fordiscovering hidden or non-obvious semantic structures within a body oftext. As an example, certain words in a document about a particulartopic may occur more frequently than others. To continue the example,words such as “nuts” and “bolts” are more likely to occur in a documentwhose primary topic is related to threaded fasteners. Likewise, wordssuch as “steel” and “aluminum” are more likely to occur in a documentwhose primary topic is related to the properties of certain metals. Tofurther continue the example, a document is typically associated withvarious topics in different proportions. Consequently, a document thatis 20% about the properties of certain metals and 80% about their use infasteners would likely contain four times as many fastener-related wordsthan metal-related words.

Accordingly, the resulting clusters, or nodes, of similar words in suchmodels represent the various topics within a given document. In variousembodiments, a topic model is implemented as a mathematical framework,and based upon the statistical distribution of words within each, notonly discovers each topic in a document, but also their relativeconcentration and distribution. Topic models are also known to bereferred to as probabilistic topic models, which broadly refer tostatistic algorithms used for discovering latent semantic structureswithin an extensive body of text, such as a corpus of content.

As used herein, domain topic abstraction broadly refers to knowledgeelements of an information domain organized in an abstraction hierarchyor taxonomy, where instances of knowledge elements in proximate classesare similar. As such, an abstraction hierarchy broadly refers to agrouping principle, whereby a hierarchy is adhered to with higher levelsof abstraction placed near the top of the hierarchy and more specificconcepts are placed lower down. Likewise, as used herein, a taxonomybroadly refers to a classification of things, knowledge elements, orconcepts associated with a certain domain of information, as well as theprinciples underlying such a classification.

In various embodiments, a particular taxonomy may be implemented tosupport relationship schemes other than parent-child hierarchies, suchas network structures. In certain embodiments, these network structuresmay include various cognitive graphs, described in greater detailherein. In these embodiments, one or more taxonomies may be implementedto include single children with multiple parents. As an example, “bolt”may appear within a network structure with parents that include“fasteners,” “connectors,” and so forth.

Skilled practitioners of the art will be aware that taxonomies aregenerally considered to be narrower than ontologies within thediscipline of knowledge management, as ontologies typically apply to alarger variety of relation types. Those of skill in the art willlikewise be aware that a hierarchical taxonomy, within the field ofmathematics, is typically considered a tree structure of classificationsfor a given set of objects. As such, it is also commonly referred to asa containment hierarchy. At the top of such structures is a singleclassification, the root node, which applies to all objects below it.More particularly, subordinate tiers of nodes in the hierarchy containprogressively more detailed classifications associated with varioussubsets of a total set of classified objects. Accordingly, theclassification of objects proceeds from being more general towards thetop of the hierarchy to being more specific towards its lower levels.

As likewise used herein, a hierarchical topic model 1100 broadly refersto an algorithmic approach to discovering topics occurring within acorpus, determining their respective degree of abstraction, andstructuring them accordingly into a hierarchy. In various embodiments,higher levels of abstraction for a particular topic are placed near thetop of the hierarchy and more specific levels of abstraction are placedlower in the hierarchy. In certain embodiments, a topic's level ofabstraction, and its associated placement within a hierarchy, isdiscovered automatically. In various embodiments, the resultinghierarchy is generated in a taxonomic form familiar to those of skill inthe art.

Referring now to FIG. 11 , a corpus of content is processed to identifya set of domain topics 1102, which in various embodiments arehierarchically abstracted 1116 into a hierarchical topic model 1100,described in greater detail herein. In these embodiments, domain topics1102 that have a higher degree of abstraction, or less specificity, arehierarchically abstracted 1116 into the upper levels of the hierarchicaltopic model 1100. Likewise, domain topics having a lesser degree ofabstraction, or more specificity, are hierarchically abstracted 1116into lower levels. As an example, the domain topic 1124 in abstractionlevel al₁ 1104 in the hierarchical topic model 1100 has a higher degreeof abstraction, or less specificity, than domain topics 1134 and 1136 inabstraction level al₁ 1106. Likewise, the domain topics 1150, 1152,1154, and 1156 in abstraction level al₄ 1112 have a lower degree ofabstraction, or more specificity, than domain topics 1140, 1142, 1144,1146, and 1148 in abstraction level al₃ 1108. To continue the example,domain topics 1160, 1162, 1164, 1166, and 1168 in abstraction level al₅1112 have a higher degree of abstraction, or less specificity, thandomain topics 1170 through 1178 in abstraction level al_(n) 1114, and soforth.

In various embodiments, individual domain topics 1102 are automaticallyidentified and hierarchically abstracted 1116 into a correspondingabstraction level, such as abstraction levels al₁ 1104, al₂ 1106, al₃1108, al₄ 1110, al₅ 1112, and a/n 1114 shown in FIG. 11 , according totheir associated attributes. In one embodiment, attributes associatedwith a particular domain topic 1102 are in the form of data elementsstored in a database, such as a relational database. In anotherembodiment, the attributes are in the form of knowledge elements storedin a knowledge repository, such as a cognitive graph. In yet anotherembodiment, the attributes are in the form of metadata. In yet stillanother embodiment, the attributes are derived from processing image,video or audio data. In one embodiment, the attributes are derived fromsocial media data associated with a particular set of users. Skilledpractitioners of the art will recognize that many such examples ofdomain topic 1102 attributes are possible. Accordingly, the foregoing isnot intended to limit the spirit, scope or intent of the invention.

As an example, a manufacturer of industrial fasteners may havetens-of-thousands of individual products in their product line, eachwith various attributes such as length, diameter, width, threadcharacteristics, head type, grades and materials, mechanical properties,prices, and so forth. In this example, domain topics 1170 through 1178in abstraction level al_(n) 1114 may be the lowest level of abstractionwithin the hierarchical topic model 1100 that includes all productshaving attributes of both “screw” and “metal.” To continue the example,the application of a domain topic abstraction algorithm, described ingreater detail herein, to the domain topics 1170 through 1178 inabstraction level al_(n) 1114 may result in the hierarchical abstractionof domain topics 1160, 1162, 1164, 1166, and 1168 into abstraction levelal₅ 1112. In continuance of the example, domain topics 1160 and 1162 mayrespectively relate to wood screws and machine screws manufactured frombrass. Likewise, domain topic 1164 may relate to stainless steel machinescrews, while domain topics 1166 and 1168 may respectively relate tozinc-plated steel wood screws and machine screws.

In further continuance of the example, the domain topic abstractionalgorithm may be applied to domain topics 1160, 1162, 1164, 1166, and1168 in abstraction level al₅ 1112, resulting in the hierarchicalabstraction of domain topics 1150, 1152, 1154, and 1156 into abstractionlevel al₄ 1110. To continue the example, domain topics 1150 and 1152 mayrespectively relate to brass wood screws and machine screws. Likewise,domain topic 1154 may relate to stainless steel machine screws, whiledomain topics 1156 may relate to zinc-plated steel screws of differenttypes.

To continue the example, the domain topic abstraction algorithm may thenbe applied to domain topics 1150, 1152, 1154, and 1156 in abstractionlevel al₄ 1110, resulting in the hierarchical abstraction 1116 of domaintopics 1144, 1146, and 1148 into abstraction level al₃ 1108. To continuethe example further, domain topics 1144, 1146 and 1148 may respectivelyrelate to screws of various types manufactured from brass, stainlesssteel, and zinc-plated steel. Likewise, domain topics 1140 and 1142 mayrespectively relate to various types of threaded fasteners manufacturedfrom nylon and wood, which are abstracted from other domain topics 1102not shown in FIG. 11 .

To further continue the example, the domain topic abstraction algorithmmay then be applied to domain topics 1140, 1142, 1144, 1146, and 1148 inabstraction level al₃ 1108, resulting in the hierarchical abstraction ofdomain topics 1134 and 1136 in abstraction level al₂ 1106. In furthercontinuance of the example, domain topics 1134 and 1136 in abstractionlevel al₂ 1106 may respectively relate to threaded fastenersmanufactured from various types of brass and steel, whether zinc-platedsteel or stainless steel. To continue the example even further, thedomain topic abstraction algorithm may once again be applied to domaintopics 1134 and 1136 in abstraction level al₂ 1106, which results in thehierarchical abstraction of domain topic 1124 in level al₁ 1104. Tocomplete the example, the domain topic 1124 in level al₁ 1104 may relateto threaded fasteners of various kinds, as opposed to various types ofnon-threaded fasteners, such as pop rivets and cotter pins.

In certain embodiments, domain topics are hierarchically abstracted 1116from lower levels of the hierarchical topic model 1100 according totheir domain topic relevance distribution 1118. As used herein, domaintopic relevance distribution 1118 broadly refers to the statisticaloccurrence of a particular domain topic 1102 within a corpus of content.In various embodiments, the domain topic relevance distribution 1118 fora particular domain topic 1102 associated with a particular abstractionlevel, such as al₁ 1104, al₂ 1106, al₃ 1108, al₄ 1110, al₅ 1112, andal_(n) 1114, is assigned a corresponding domain topic relevancedistribution 1118 value, such as r₁, r₂, r₃, r₄, and r₅.

For example, domain topics 1168 and 1166 may share a common attribute,such as both being related to screws manufactured from zinc-platedsteel, yet they may have certain distinct attributes, such asrespectively being related to wood screws and machine screws. Likewise,the statistical occurrence of domain topic 1168 is higher than that ofdomain topic 1166. Accordingly, as depicted in FIG. 11 , the domaintopic relevance distribution 1118 value r, assigned to domain topic 1168is higher than the domain topic relevance distribution 1118 value r₄assigned to domain topic 1166. In one embodiment, the domain topicrelevance distribution 1118 values associated with any given abstractionlevel are normalized to sum to a value of 1.0. As an example, the domaintopic relevance distribution 1118 values respectively associated withdomain topics 1160, 1162, 1164, 1166 and 1198 in abstraction level al₅1112 may be 0.1, 0.1, 0.3, 0.1 and 0.4.

In various embodiments, continuous learning operations 1198 areperformed by iteratively applying a domain topic abstraction algorithmto a corpus of content. In certain embodiments, the continuous learningoperations 1198 are performed through the use of upwards-downwards Gibbssampling, described in greater detail herein. In various embodiments,the hierarchical topic model 1100 is implemented as an augmented GammaBelief Network (GBN), likewise described in greater detail herein. Inthese embodiments, the number of iterations used when applying thedomain abstraction level is a matter of design choice.

In certain embodiments, user input is processed to determine the numberof abstraction levels, and the number of domain topics 1102 eachcontains. In these embodiments, the number of abstraction levels, andthe number of domain topics 1102 each contains, is a matter of designchoice. Those of skill in the art will recognize that many suchembodiments and examples are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

FIG. 12 is a simplified block diagram of cognitive machine learningoperations performed in accordance with an embodiment of the inventionto navigate a hierarchical abstraction of topics within a corpus. Invarious embodiments, a cognitive inference and learning system (CILS)utilizes a cognitive machine learning algorithm, as described in greaterdetail herein, to perform cognitive machine learning operations. Incertain embodiments, the cognitive machine learning algorithm may beimplemented as a hierarchical topic navigation algorithm.

In various embodiments, the hierarchical abstraction of topics isimplemented in the form of a hierarchical topic model 1200, described ingreater detail herein. In certain embodiments, the hierarchical topicmodel is implemented in the form of an abstraction hierarchy, likewisedescribed in greater detail herein. In various embodiments, the corpusof content may include a collection of documents, a text, an image, anaudio recording, a video recording, streaming media (e.g., newsfeeds), asocial media post, database elements, various kinds of metadataassociated with the foregoing, or some combination thereof.

In these embodiments, the hierarchical topic navigation algorithm isimplemented to assist various classes of users, or user types, tohierarchically navigate 1216 a particular hierarchical topic model 1200.In one embodiment, the user type is associated with a cognitive persona,described in greater detail herein. In another embodiment, the user isuniquely associated with a cognitive profile, also referred to as “aprofile of one,” likewise described in greater detail herein. In theseembodiments, the method by which an individual user is determined to beassociated with a given cognitive persona or cognitive profile, and byextension, a particular hierarchical topic model 1200, is a matter ofdesign choice.

Referring now to FIG. 12 , a hierarchical topic model 1200 is processedto identify a set of domain topics 1202 associated with the highestabstraction level associated with a particular domain of information. Invarious embodiments, the domain of information is determined byprocessing a user query. In certain embodiments, the resulting set ofdomain topics 1202 associated with the identified highest abstractionlevel is presented to a user within a window 1284 of a user interface(UI).

To extend a prior example, a user may want to peruse a large corpus ofindustrial fasteners to identify the most applicable fastener for aparticular need. However, they are not sure which type of type offastener is best suited for their needs. In this example, the user firstsubmits a query related to industrial fasteners to a CILS. In response,domain topics 1220, 1222, 1224, 1226, and 1226 in abstraction levelal_(l) 1204, which may be the highest level of abstraction forindustrial fasteners within the hierarchical topic model 1200, aredisplayed within the UI window 1284. In continuance of this example,domain topics 1220, 1222, 1224, 1226, and 1228 may respectively relateto threaded fasteners (e.g., screws), non-threaded fasteners (e.g.,rivets), conformant fasteners (e.g., cam locks), clips (e.g., retainingrings), and clamps (e.g., hose clamp). To further continue this example,the user may select domain topic 1220, related to threaded fasteners,through the use of a user gesture familiar to those of skill in the artwithin the UI window 1284.

As a result, the user's selection of domain topic 1220 is used by ahierarchical topic navigation algorithm to determine the next lowestabstraction level for threaded fasteners in the hierarchical topic model1200. To continue this example, domain topics 1230, 1232, 1234, 1236,and 1238 in abstraction level al₂ 1206, which may be the next lowestabstraction level in the hierarchical topic model 1200 that relates tothreaded fasteners, are displayed within the UI window 1284. In furthercontinuance of this example, domain topics 1230, 1232, 1234, 1236, and1238 may respectively relate to bolts, threaded rods, screws, nuts, andthreaded anchors of various kinds. To continue this example, the usermay select domain topic 1234, related to screws, within the UI window1284.

Accordingly, the user's selection of domain topic 1234 is likewise usedby the hierarchical topic navigation algorithm to determine the nextlowest abstraction level in the hierarchical topic model 1200, and itsassociated domain topics 1202. In continuance of this example, domaintopics 1240, 1242, 1244, 1246, and 1248 in abstraction level al₃ 1208,which may be the next lowest abstraction level in the hierarchical topicmodel 1200 that relates to screws, are displayed within the UI window1284. In further continuance of this example, domain topics 1240, 1242,1244, 1246, and 1248 may respectively relate to bolts, threaded rods,screws, nuts, and machine screws. To continue this example, the user maylikewise select domain topic 1248, related to machine screws, within theUI window 1284.

As before, the user's selection of domain topic 1248 is then used by thehierarchical topic navigation algorithm to determine the next lowestabstraction level of the hierarchical topic model 1200, and itsassociated domain topics 1202. To further continue this example, domaintopics 1250, 1252, 1254, 1256, and 1258 in abstraction level al₄ 1210,which may be the next lowest abstraction level in the hierarchical topicmodel 1200 that relates to machine screws, are displayed within the UIwindow 1284. In further continuance of this example, domain topics 1250,1252, 1254, 1256, and 1258 may respectively relate to machine screwsmade from brass, zinc-plated steel, stainless steel, hardened steel, andnylon. In this example, the user may then select domain topic 1254,related to machine screws made from stainless steel, within the UIwindow 1284.

Once again, the user's selection of domain topic 1254 is used by thehierarchical topic navigation algorithm to determine the next lowestabstraction level of the hierarchical topic model 1200, and itsassociated domain topics 1202. To continue this example, domain topics1260, 1262, 1264, 1266, and 1268 in abstraction level al₅ 1212, whichmay be the next lowest abstraction level in the hierarchical topic model1200 that relates to machine screws made from stainless steel, aredisplayed within the UI window 1284. To further continue this example,domain topics 1260, 1262, 1264, 1266, and 1268 may respectively relateto round head, oval head, pan head, truss head, and flat head stainlesssteel machine screws. To continue this example further, the user mayselect domain topic 1268, related to flat head stainless steel machinescrews, within the UI window 1284.

Accordingly, the user's selection of domain topic 1268 is once againused by the hierarchical topic navigation algorithm to determine thenext lowest abstraction level of the hierarchical topic model 1200, andits associated domain topics 1202. To complete this example, domaintopics 1270 through 1278 in abstraction level al₆ 1214, which may be thenext lowest abstraction level in the hierarchical topic model 1200 thatrelates to flat head machine screws made from stainless steel, aredisplayed within the UI window 1284. The process is then repeated untilthe user has navigated 1216 the hierarchical topic model 1200 to theabstraction level that contains domain topics 1202 with sufficientspecificity to satisfy their needs.

In various embodiments, the number of abstraction levels, and the numberof domain topics 1202 each contains, is determined by the domain topicrelevance distribution 1218 value, such as r₁, r₂, r₃, r₄, and r₅,associated with each domain topic 1202 within a particular abstractionlevel. In these embodiments, the domain topic relevance distribution1218 value that qualifies a particular domain topic 1002 for inclusionwithin a particular abstraction level is a matter of design choice. Incertain embodiments, the number of abstraction levels, and the number ofdomain topics 1202 each contains, is user-defined. In variousembodiments, the number of domain topics 1202 a user may select within aUI window 1284 likewise user-defined. In these embodiments, the numberof abstraction layers, or the number of domain topics 1202 eachcontains, is a matter of design choice.

In various embodiments, domain topics 1202 are iterative presented to auser in a gamified context as the hierarchical topic model 1200 isnavigated. As an example, the user may receive points for each selectionthey make. In this example, the points may be redeemed if the user makesa purchase. As another example, interesting facts or trivia associatedwith a given domain topic 1202 may be provided to the user as the resultof the user making a selection. In these embodiments, the method ofgamifying the domain topic 1202 selection process, and the method bywhich the hierarchical topic model 1200 is navigated, is a matter ofdesign choice. Skilled practitioners of the art will recognize that manysuch embodiments and examples related to gamification of the domaintopic 1202 selection process, and the navigation of the hierarchicaltopic model 1200, are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

In various embodiments, the user's selection of a particular domaintopic 1220 in a given level of abstraction within the hierarchical topicmodel 1200 generates training data. In certain embodiments, the trainingdata is then used as input to a domain topic abstraction algorithm,described in greater detail herein, to generate alternative versions ofthe hierarchical topic model 1200. In various embodiments, thealternative versions of the hierarchical topic model 1200 are thenassociated with an individual user or classes of users for subsequentuse. As described in greater detail herein, the individual user may berepresented by a cognitive persona or a cognitive profile, likewisedescribed in greater detail herein. In certain of these embodiments, thealternative version of the hierarchical topic model is associated withthe cognitive persona or a cognitive profile. In various embodiments,continuous learning operations 1298 are performed by iterativelyproviding training data to the domain topic abstraction algorithm. Inthese embodiments, the method by which the alternative version of thehierarchical topic model 1200 is associated with the cognitive personaor a cognitive profile, and the method by which the training data isprovided to the domain abstraction algorithm, is a matter of designchoice.

As an example, a user may be identified as being associated with acertain class of user. Accordingly, the user is presented a particularhierarchical topic model 1200 typically associated with that class ofuser. As a result, the user may have successively selected domain topics1220, 1234, 1248, 1254 and 1268, which respectively correspond tothreaded fasteners, screws, machine screws, stainless steel machinescrews, and flat head stainless steel machine screws. However, the usermay have decided thereafter to not navigate 1216 the hierarchical topicmodel 1200 to any lower abstraction level. Instead, the user navigates1216 back up to abstraction level al₄ 1210, where domain topic 1252,which relates to zinc-plated machine screws, is selected. Additionaltraining data is generated as a result of the selection, which in turnis provided to the hierarchical topic navigation algorithm forprocessing.

Accordingly, domain topics in a different abstraction level (not shown)are presented to the user. In this example, the domain topics may relateto round head, oval head, pan head, truss head, and flat headzinc-plated steel machine screws. In turn, the user may select thedomain topic related to flat head zinc-plated machine screws, only tonavigate 1216 once again to abstraction level al₄ 1210. Once there, theuser may select domain topic 1250, which relates to brass machinescrews. As before, additional training data is generated as a result ofthe user's selection and is provided to the domain topic abstractionalgorithm for processing. Those of skill in the art will recognize thatmany such embodiments and examples are possible. Accordingly, theforegoing is not intended to limit the spirit, scope or intent of theinvention.

FIG. 13 is a simplified block diagram of cognitive machine learningoperations performed in accordance with an embodiment of the inventionto determine the prevalence of various terms within a corpus of contentat certain intervals during a temporal sequence of events. In variousembodiments, a cognitive inference and learning system (CILS) utilizes acognitive machine learning algorithm, as described in greater detailherein, to perform the cognitive machine learning operations. In certainembodiments, the cognitive machine learning algorithm may be implementedas a temporal topic discovery algorithm. In various embodiments, theprevalence of various terms during a temporal sequence is determined byiteratively applying the temporal topic discovery algorithm at certainintervals to a corpus of content associated with a particular domain ofinformation. In these embodiments, the corpus of content may include acollection of documents, a text, an image, an audio recording, a videorecording, streaming media (e.g., newsfeeds), a social media post,database elements, various kinds of metadata associated with theforegoing, or some combination thereof.

As used herein, a term, as it relates to machine learning operations,broadly refers to a semantic unit within a corpus of content. In certainembodiments, a term may be a word, an image, a phoneme, a data element,or various kinds of metadata associated with any of the foregoing. Invarious embodiments, a term within a corpus of content may be associatedwith one or more topics, described in greater detail herein. As anexample, the corpus may contain the terms “diesel,” “tank,” “fuel,”“stored,” “armored,” and “vehicle.” Dependent upon the respectiveprevalence of these terms within the corpus, the primary topic may be“diesel fuel storage” or “diesel-powered armored vehicles.”

To continue the example, the corpus may contain a sentence such as,“Diesel fuel, which is commonly used to power a large vehicle, may bestored in an armored storage tank for safety reasons.” Likewise, thecorpus instead may also contain a sentence such as, “An armored vehicle,such as a tank, is typically powered by an engine that burns dieselfuel, which is generally stored in the vehicle's main fuel tank.” Inthis example, the topic of the first sentence is related to “diesel fuelstorage,” while the topic of the second sentence is related to“diesel-powered armored vehicles.”

To further continue the example, both topics may be associated with thesame prevalence of the terms “diesel” and “fuel.” However, the topic“diesel fuel storage” may be associated with a higher prevalence of theterms “tank” and “stored.” Likewise, the topic “diesel-powered armoredvehicles” may be associated with a higher prevalence of the terms“armored” and “vehicle.” Furthermore, the overall prevalence of theterms “tank” and “stored” within the corpus may be greater than theprevalence of the terms “armored” and “vehicle.” Accordingly, “dieselfuel storage” would be the preeminent topic within the corpus.

In various embodiments, the topics may include event topics 1302. Asused herein, an event topic 1302 broadly refers to a topic associatedwith a particular event 1318, such as events e₁ 1306, e₂ 1308, e; 1310,e₄ 1312, e₅ 1314 through e_(n) 1316 in the temporal sequence of events1320 shown in FIG. 13 . As likewise used herein, a temporal sequence ofevents 1320 broadly refers to a chronological sequence of events 1318occurring at a certain time intervals ‘t’ 1322. In various embodiments,the time intervals ‘t’ 1322 occur within a defined period of time, suchas a time window ‘w’ 1324. An event 1318, as used herein, broadly refersto an occurrence of a particular point in time. Likewise, a time window,as used herein, broadly refers to a period of time defined by a firstevent 1318 and a second event 1318. For example, as shown in FIG. 13 , atime window ‘w’ 1324 may be defined by a first event e₁ 1306 and asecond event e₅ 1314, separated by time intervals ‘t’ 1322.

In certain embodiments, a time window ‘w’ 1324 is implemented toiteratively advance at time interval ‘t’ 1322 to provide a slidingwindow ‘w’+‘t’ 1326 within a temporal sequence of events 1320. Forexample, the time window ‘w’ 1324 shown in FIG. 13 may include events e₁1306 through e₅ 1314, which may be iterated by time interval ‘t’ 1322 toinclude event topics e₂ 1308 through e_(n) 1316. In this example, theevent topics 1302 associated with event e₁ 1306 are no longer includedin the temporal sequence of events 1320 upon the execution of slidingwindow ‘w’+‘t’ 1326, but the event topics 1302 associated with eventtopics e_(n) 1316 are.

In various embodiments, the prevalence of various terms within a corpusof content at certain intervals during a temporal sequence of events1320 is generated in the form of a temporal topic model 1300. Likewise,as used herein, a temporal topic model 1300 broadly refers to a topicmodel extended to accommodate various temporal aspects, such as two ormore events 1318. As an example, the preeminence of various topicswithin a temporal topic model 1300, and their respective correlation toone another, may change over time, such as topics related to theactivities of one political candidate or another during the course oftheir respective campaigns.

In certain embodiments, a corpus of content is iteratively processed attime interval t 1322 during a temporal sequence of events 13200 toidentify the relative preeminence of event topics 1302 associated withvarious events 1318. For example, as shown in FIG. 13 , event e₁ 1306includes event topics 1330, 1332, 1334, 1336 and 1338, while event e₂1308 includes event topics 1340, 1342, 1344, 1346 and 1348. Likewise,event e₃ 1310 includes event topics 1350, 1352, 1354, 1356 and 1358,while event e₄ 1312 includes event topics 1360, 1362, 1364, 1366 and1368. As likewise shown in FIG. 13 , event e₅ 1314 includes event topics1370, 1372, 1374, 1376 and 1378, while event e_(n) 1316 includes eventtopics 1380 through 1388.

In these embodiments, the relative preeminence of one event topic 1302to another is determined by the relative prevalence of their associatedterms within the corpus of content at that particular event 1318 in thetemporal sequence of events 1320. As an example, event topic 1330 atevent e₁ 1306 is preeminent to event topics 1332, 1334, 1336 and 1338.Likewise, event topic 1334 at event e₂ 1306 is preeminent to event topic1348, which in turn is preeminent to event topics 1340, 1342 and 1346.To continue the example, event topic 1350 at event e₃ 1310 is preeminentto event topic 1354, which in turn is preeminent to event topics 1352,1356 and 1358. Likewise, event topic 1354 at event e₄ 1312 is preeminentto event topics 1360 and 1368, both of which are preeminent to eventtopics 1362 and 1366. To further continue the example, event topic 1370at event e₅ 1314 is preeminent to event topic 1374, which in turn ispreeminent to event topics 1372, 1376 and 1378, continuing on to eventtopics 1380 through 1399 at event e_(n) 1316.

In various embodiments, the number of event topics 1302 associated witheach event 1318 is determined according to their event topic relevancecorrelation 1328. As used herein, event topic relevance correlation 1328broadly refers to the statistical correlation of a particular eventtopic 1302 to other event topics 1302 within a corpus of content at thetime of a particular event 1318 in a temporal sequence of events 1320.In certain embodiments, the event topic relevance correlation 1328 for aparticular event topic 1302 associated with a particular event, such ase₁ 1306, e₂ 1308, e₃ 1310, e₄ 1312, e₅ 1314 through e_(n) 1316, isassigned a corresponding event topic relevance correlation 1328 value,such as r₁, r₂, r₃, r₄, and r₅. In certain of these embodiments, theevent topic relevance correlation 1328 for various event topics 1302associated with a corresponding event 1318 is automatically determinedby iteratively applying a temporal topic discovery algorithm at certaintime intervals t 1322 to a corpus of content.

For example, event topics 1360 and 1364 at event e₄ 1312 may berespectively associated with the topics “diesel fuel storage” and“diesel-powered armored vehicles.” Likewise, event topics 1370 and 1374at event e₅ 1314 may also be respectively associated with the topics“diesel fuel storage” and “diesel-powered armored vehicles.” In thisexample, event topics 1360, 1364, 1370, and 1374 may all share a set ofcommon terms, such as “diesel,” and “fuel,” which respectively have anequivalent prevalence in a corpus of content at the time of event e₄1312 and e₅ 1314. However, event topic 1360 may have a lesser prevalenceof the terms “tank” and “stored” compared to the prevalence of terms“armored” and “vehicle,” which may be associated with event topic 1376.Likewise, event topic 1370 may have a greater prevalence of the terms“tank” and “stored” compared to the prevalence of the terms “armored”and “vehicle,” which may be associated with event topic 1374.

Accordingly, the event topic relevance correlation value r₁ assigned toevent topic 1370 would be higher than the event topic relevancecorrelation value r₃ assigned to event topic 1372. Consequently, atevent e₅ 1314, the topic “diesel fuel storage,” associated with eventtopic 1370 would have a greater event topic relevance correlation thanthe topic “diesel-powered armored vehicles” associated with event topic1374. As a result, the event topic “diesel fuel storage,” which was notpreeminent at event e₄ 1312, becomes the preeminent event topic at evente₅ 1314. In these embodiments, the event topic relevance correlation1328 value that qualifies a particular domain topic 1302 for associationwith a particular event 1318 is a matter of design choice. Skilledpractitioners of the art will recognize that many such embodiments andexamples are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

FIG. 14 is an inter-topic distance map depicting the distribution ofterms associated with a particular topic at a first event in a temporalsequence implemented in accordance with an embodiment of the invention.In this embodiment, a corpus of content includes news articlesassociated with a first temporal sequence event 1412 that occurred onNov. 13, 2015, which was the date of a terrorist attack in Paris,France. As described in greater detail herein, the corpus of content isfirst processed to identify the most frequently used terms it contains.The identified terms are then processed to identify related domaintopics, which are then ranked according to their preeminence within thecorpus.

Once ranked, the domain topics are then graphically plotted on aninter-topic distance map 1402 according to the variability of theirrespective topic-term distribution 1406. As shown in FIG. 14 , theinter-topic distance map includes two axes, PC₁ 1408 and PC₂ 1410. Inthis embodiment, the PC₁ 1408 axis depicts domain topics that have themost variability while the PC₂ 1410 axis depicts domain topics that havethe second-most variability. Accordingly, the placement of a domaintopic's graphical representation on the inter-topic distance map 1402provides a visual indication of the respective variability of a givendomain topic. As likewise shown in FIG. 14 , the relative ranking ofeach domain topic is depicted according to the size of its respectivegraphical representation.

In this embodiment, the selection of a graphical representation of adomain topic results in a graphical presentation of the most relevantterms 1414 for the selected topic. Likewise, as shown in FIG. 14 , theterm frequency for the selected topic 1416 and for the corpus overall1418 are graphically depicted for the selected domain topic. Forexample, selection of the graphical representation 1404 for the domaintopic “Breaking news on Paris attacks.” is depicted as being the 32^(nd)least preeminent topic within the corpus at the time of the temporalsequence event 1412. However, one of its related terms, “attack,” hasthe highest term frequency for the corpus overall 1518

FIG. 15 is an inter-topic distance map depicting the distribution ofterms associated with a particular topic at a second event in a temporalsequence implemented in accordance with an embodiment of the invention.In this embodiment, a corpus of content includes news articlesassociated with a second temporal sequence event 1512 that occurred onNov. 14, 2015, which was the day after a terrorist attack in Paris,France. As before, the corpus of content is first processed to identifythe most frequently used terms it contains. The identified terms arethen processed to identify related domain topics, which are then rankedaccording to their current preeminence within the corpus.

Once ranked, the domain topics are then graphically plotted on aninter-topic distance map 1502 according to their respective topic-termdistribution 1506. As shown in FIG. 15 , the inter-topic distance mapincludes two axes, PC₁ 1508 and PC₂ 1510. In this embodiment, the PC₁1508 axis depicts domain topics that have the most variability while thePC₂ 1510 axis depicts domain topics that have the second-mostvariability. Accordingly, the placement of a domain topic's graphicalrepresentation on the inter-topic distance map 1502 provides a visualindication of the respective variability of a given domain topic. Aslikewise shown in FIG. 15 , the relative ranking of each domain topic isdepicted according to the size of its respective graphicalrepresentation.

In this embodiment, the selection of a graphical representation of adomain topic results in a graphical presentation of the most relevantterms 1514 for the selected topic. Likewise, as shown in FIG. 15 , theterm frequency for the selected topic 1516 and for the corpus overall1518 are graphically depicted for the selected domain topic. Forexample, selection of the graphical representation 1504 for the domaintopic “Paris attacks kill more than 100 in seemingly coordinated terrorstrike.” is depicted as being the most preeminent topic within thecorpus at the time of the second temporal sequence event 1512.Consequently, one of its related terms, “attack,” has the highest termfrequency for both the selected topic 1516 and for the corpus overall1518, followed only by the term “bombing.”

FIG. 16 is an inter-topic distance map depicting the distribution ofterms associated with a particular topic at a third event in a temporalsequence implemented in accordance with an embodiment of the invention.In this embodiment, a corpus of content includes news articlesassociated with a third temporal sequence event 1612 that occurred onNov. 15, 2015, which was the second day after a terrorist attack inParis, France. As before, the corpus of content is first processed toidentify the most frequently used terms it contains. The identifiedterms are then processed to identify related domain topics, which arethen ranked according to their current preeminence within the corpus.

Once ranked, the domain topics are then graphically plotted on aninter-topic distance map 1602 according to their respective topic-termdistribution 1606. As shown in FIG. 16 , the inter-topic distance mapincludes two axes, PC₁ 1608 and PC₂ 1610. In this embodiment, the PC₁1608 axis depicts domain topics that have the most variability while thePC₂ 1610 axis depicts domain topics that have the second-mostvariability. Accordingly, the placement of a domain topic's graphicalrepresentation on the inter-topic distance map 1602 provides a visualindication of the respective variability of a given domain topic. Aslikewise shown in FIG. 16 , the relative ranking of each domain topic isdepicted according to the size of its respective graphicalrepresentation.

In this embodiment, the selection of a graphical representation of adomain topic results in a graphical presentation of the most relevantterms 1614 for the selected topic. Likewise, as shown in FIG. 16 , theterm frequency for the selected topic 1616 and for the corpus overall1618 are graphically depicted for the selected domain topic. Forexample, selection of the graphical representation 1604 for the domaintopic “Assailant in Paris attacks identified, relatives questioned.Paris attack probe spreads: manhunt in Belgium.” is depicted as beingthe most preeminent topic within the corpus at the time of the thirdtemporal sequence event 1612. Consequently, one of its related terms,“attack,” has the highest term frequency for both the selected topic1516 and for the corpus overall 1518, followed only by the term“bombing.”

FIG. 17 is an inter-topic distance map depicting the distribution ofterms associated with a particular topic at a fourth event in a temporalsequence implemented in accordance with an embodiment of the invention.In this embodiment, a corpus of content includes news articlesassociated with a fourth temporal sequence event 1712 that occurred onNov. 16, 2015, which was the third day after a terrorist attack inParis, France. As before, the corpus of content is first processed toidentify the most frequently used terms it contains. The identifiedterms are then processed to identify related domain topics, which arethen ranked according to their current preeminence within the corpus.

Once ranked, the domain topics are then graphically plotted on aninter-topic distance map 1702 according to their respective topic-termdistribution 1706. As shown in FIG. 17 , the inter-topic distance mapincludes two axes, PC₁ 1708 and PC₂ 1710. In this embodiment, the PC₁1708 axis depicts domain topics that have the most variability while thePC₂ 1710 axis depicts domain topics that have the second-mostvariability. Accordingly, the placement of a domain topic's graphicalrepresentation on the inter-topic distance map 1702 provides a visualindication of the respective variability of a given domain topic. Aslikewise shown in FIG. 17 , the relative ranking of each domain topic isdepicted according to the size of its respective graphicalrepresentation.

In this embodiment, the selection of a graphical representation of adomain topic results in a graphical presentation of the most relevantterms 1714 for the selected topic. Likewise, as shown in FIG. 17 , theterm frequency for the selected topic 1716 and for the corpus overall1718 are graphically depicted for the selected domain topic. Forexample, selection of the graphical representation 1704 for the domaintopic “Billions wiped off travel shares after Paris attacks, overallmarket steady.” is depicted as being the most preeminent topic withinthe corpus at the time of the fourth temporal sequence event 1712.Consequently, one of its related terms, “attack,” has the second highestterm frequency for the selected topic 1516 and the highest for thecorpus overall 1718. However, now the term “European” has the highestterm frequency for the selected topic 1716 and the second highest forthe corpus overall 1718.

FIG. 18 is a simplified block diagram of the performance of continuouscognitive machine learning operations implemented in accordance with anembodiment of the invention. In various embodiments, a cognitiveinference and learning system (CILS) is implemented to utilize ahierarchical topical 1804 model, a temporal topic 1806 model, and aranked insight 1808 model, or some combination thereof, to performcontinuous cognitive machine learning 1810 operations.

In these embodiments, feedback on observations 416, decisions 418, andresults 420, described in greater detail in the descriptive textassociated with FIG. 4 , is used in the performance of the continuouscognitive machine learning 1810 operations. In certain embodiments, thehierarchical topical 1804 model, a temporal topic 1806 model, and aranked insight 1808 model, or some combination thereof, are implementedto exchange data amongst themselves to perform the continuous cognitivemachine learning 1810 operations. In various embodiments, thehierarchical topical 1804 model, a temporal topic 1806 model, and aranked insight 1808 model, or some combination thereof, are implementedto interact with one another to perform the continuous cognitive machinelearning 1810 operations. In certain embodiments, the continuouscognitive machine learning 1810 operations are performed to generateranked cognitive insights 1820, likewise described in greater detailherein.

In various embodiments, the hierarchical topical 1804 model isimplemented through the utilization of a domain topic abstraction 1812algorithm and a hierarchical topic navigation 1814 algorithm. In certainembodiments, the temporal topic 1806 model is implemented through theutilization of a temporal topic discovery algorithm 1816. In variousembodiments, the ranked insight 1804 model is implemented through theutilization of a factor-needs 1818 algorithm configured to performranking/re-ranking operations. In certain embodiments, the factor-needsalgorithm 1818 is implemented to provide ranked insights 1820 to a user1802 resulting from the performance of such ranking/re-rankingoperations. In various embodiments, these algorithms may be used invarious combinations to perform the continuous cognitive machinelearning 1810 operations. The method by which these algorithms are usedin combination is a matter of design choice. Skilled practitioners ofthe art will recognize that many such embodiments are possible.Accordingly, the foregoing is not intended to limit the spirit, scope orintent of the invention.

In various embodiments, a factor-needs 1820 algorithm is implemented togenerate a needs graph model. In certain embodiments, the needs graphmodel utilizes a matrix approach to map various users ‘U’ 1802 toparticular factors ‘F’, each of which have certain attributes ‘A’. Inturn, various attributes ‘A’ of each factor ‘F’ are mapped to certainneeds ‘N’. In various embodiments, an individual user ‘U’ 1802 may bemapped to one or more factors ‘F’. Likewise, one or more attributes ‘A’of a particular factor ‘F’ may be mapped to one or more needs ‘N’.

As used in the context of a factor-needs 1818 algorithm, a factorbroadly refers to an element of a corpus of content that has one or moreassociated attributes. As an example, a factor may be an item in aproduct catalog. As another example, a factor may be a document or othercontent element within a corpus of content, as described in greaterdetail herein. As yet another example, a factor may be a service offeredby a service provider. As likewise used in the context of a factor-needs1818 algorithm, an attribute broadly refers to a description, aparameter, a limitation, a quality, or a consideration associated with afactor. As an example, an item in a product catalog may have certainattributes describing its color, weight, price, uses, and othercharacteristics. As another example, a topic within a corpus of contentmay have certain attributes that are characterized by its associatedterms. As yet another example, a service offered by a service providermay have attributes describing its capabilities, availability,reliability, and so forth.

As likewise used in the context of the factor-needs 1818 algorithm, aneed broadly refers to a requirement, desire or objective associatedwith a user 1802. As an example, a user 1802 may have a requirement foraccommodations with disability access in a particular city on aparticular date. As another example, a user 1802 may have a desire toattend a certain type of musical performance. As yet another example, auser 1802 may have an objective of completing a series of tests to begranted certification in a particular skill. Those of skill in the artwill recognize that many such examples of factors, attributes and needsare possible. Accordingly, the foregoing is not intended to limit thespirit, scope or intent of the invention.

In various embodiments, the hierarchical topic navigation 1814 algorithmis implemented to generate training data. In certain embodiments, thetraining data is used by the factor-needs algorithm 1818 algorithm torank, or re-rank, various cognitive insights, which are provided to theuser 1802 in the form of ranked insights 1820. In one embodiment, thetraining data is used by the factor-needs algorithm 1810 to generate anew cognitive persona, which is then associated with the user 1802 or aclass of users 1802. In another embodiment, the training data is used bythe factor-needs algorithm 1810 to revise an existing cognitive personaassociated with the user 1802 or a class of users 1802. In yet anotherembodiment, the training data is used by the factor-needs algorithm 1810to generate a new cognitive profile for the user 1802. In yet stillanother embodiment, the training data is used by the factor-needsalgorithm to revise an existing cognitive profile associated with theuser 1802.

In various embodiments, the training data may include feedback fromobservations, decisions, results, or some combination thereof. Incertain embodiments, this feedback may be generated by variousinteractions 1822 between a user 1802 and a CILS. In one embodiment, thefeedback may be generated as a result of a user 1802 submitting a queryto a CILS. In another embodiment, the feedback may be generated by theprovision of external input data, such as a social media post or astreaming media feed. In yet another embodiment, the feedback may begenerated by the user 1802 navigating a hierarchical topic 1804 model,as described in greater detail herein. In certain embodiments, thefactor-needs 1818 algorithm is implemented as an online trainingalgorithm.

In various embodiments, continuous cognitive machine learning 1810operations are initiated by a CILS receiving user 1802 input. In certainembodiments, the user 1802 input may include a query, other inputrelated to a user 1802, or some combination thereof. As an example,user-related input may include information related to the user's 1802location. In this example, a determination is then made whether arelevant cognitive persona or cognitive profile, described in greaterdetail herein, is available for the user 1802. If so, then it isretrieved and then processed for use as additional user 1802 input. Theresulting user 1802 input is then processed to determine whether arelevant hierarchical topic 1804 model, or temporal topic 1806 model, ora combination of the two, are available. If a relevant temporal topicmodel 1806 is available, then the factor-needs 1818 algorithm is used toprocess it and the user input to determine relevant events and theirassociated event topics, described in greater detail herein.

A determination is then made whether a relevant hierarchical topic 1804model is available. If not, then the previously-determined relevantevent topics are processed by the factor-needs 1818 algorithm togenerate a ranked list 1820 of event topics insights. If a relevanthierarchical topic 1804 model was not available, then the ranked list1820 of event topic insights is provided to the user 1802. Otherwise,the user 1802 input, the ranked list 1820 of event topic insights, and arelevant hierarchical topic 1804 model are processed with thefactor-needs 1818 algorithm to determine the highest relevant level ofabstraction, and its associated domain topics, within the hierarchicaltopic 1804 model.

However, if it was determined that a relevant temporal topic 1816 modelwas not available, or that a relevant hierarchical topic 1804 model was,then user 1802 input and the hierarchical topic 1804 model is processedwith the factor-needs 1818 algorithm to determine the highest relevantabstraction level, and its associated domain topics, within thehierarchical topic 1804 model. The resulting highest relevantabstraction level, and its associated domain topics, is then provided tothe user 1802. A determination is then made whether the user 1802 hasselected one or more of the provided domain topics through aninteraction 1822. If so, then the user's domain topic selection(s) isprocessed to generate cognitive machine learning training data.

The needs-factor 1818 algorithm is then used to process the resultingcognitive machine learning training data, user 1802 input, ranked eventtopics (if previously generated), and the hierarchical topic 1804 model,to determine the next lowest relevant level of abstraction, and itsassociated domain topics, within the hierarchical topic 1804 model. Adetermination is then made whether the lowest level of abstractionwithin the hierarchical topic 1804 model has been reached. If not, thenext lowest level of abstraction within the hierarchical topic 1804model, and its associated domain topics, is provided to the user 1802.

A determination is then made whether the user 1802 has selected one ormore of the provided domain topics through an interaction 1822. If so,then the user's domain topic selection(s) is processed to generatecognitive machine learning training data. The needs-factor 1818algorithm is then used to process the resulting cognitive machinelearning training data, user 1802 input, ranked event topics (ifpreviously generated), and the hierarchical topic 1804 model, todetermine the next lowest relevant level of abstraction, and itsassociated domain topics, within the hierarchical topic 1804 model. Theprocess is then continued until the lowest level of abstraction in thehierarchical topic 1804 domain model is reached.

Once the lowest level of abstraction in the hierarchical topic 1804model has been reached, then the factor-needs 1810 algorithm is used torank the domain topics associated with the lowest level of abstractionwithin the hierarchical topic 1804 model. Thereafter, or if it wasdetermined that the user 1802 has not selected one or more domaintopics, then the previously ranked domain topics, or theoriginally-provided domain topics, are provided to the user as rankedinsight 1820.

As an example, patients may need to visit a distant hospital for anextended period of time to receive a repetitive or time-consumingtreatment, such as chemotherapy. As a result, they may likewise have aneed to find local restaurants that can accommodate their clinicalconditions, dietary requirements, personal preferences, and so forth. Inthis example, such needs are addressed through the use of a needs graphmodel, in which both the restaurants and the patients are described interms of certain sets of needs. To continue the example, the needsassociated with to a given restaurant may correspond to certain of itscharacteristics, such as the kind and quality of cuisine they serve,price points for a typical meal, and their distance relative to apatient's location. Likewise, the same set of needs may also describe apatient who prefers certain types of cuisines, highly-rated orhigh-quality restaurants, or venues that are nearby.

One challenge of such an approach is the requirement to pre-curatevarious needs by domain experts. Another is the lack of user 1802interactions 1822 to provide information necessary to validate theseassumptions. In continuance of the example, a pre-curated need for aparticular type of cuisine based upon the patient's gender, race,ethnicity, and clinical condition may have little correlation to thekind of foods or restaurants they may actually prefer. Likewise, arestaurant might gain popularity for a special type of food not listedin the menu, or the quality of service from the attendants.Consequently, it may be advantageous to refine the concept of variousneeds, or augment the need vocabulary with additional needs, such thatthe needs graph model can gradually evolve over time based uponinteractions 1822 with various users 1802.

To continue the example, a hospital may have a large corpus of contentrelated to short-term housing, transportation, grocery stores,restaurants, places of worship, cleaning services of various kinds,financial institutions, entertainment events, recreation venues, socialservices, and so forth. In this example, the corpus of content isabstracted into various hierarchical topic models 1804, which in turnare associated with various classes of users 1802. A new patient, oruser 1802, then interacts 1822 with a CILS to provide input related totheir various needs to a ranked insight model 1808. In turn, afactor-needs algorithm 1818 is used to process the user 1802 input todetermine the most relevant hierarchical topic model 1804, which is thenprovided to the user 1802.

The user 1802 then navigates the hierarchical topic model 1804, asdescribed in greater detail herein, to gain access to various domaintopics. In various embodiments, the user's 1802 interaction 1822 withthe hierarchical topic model 1804 is provided to the ranked insightmodel 1808, where it is used as training data. In certain embodiments,the training data is in turn processed by the factor-needs algorithm1818 to generate a list of ranked insights 1820, which is then providedto the user 1802.

To further continue the example, the user 1802 may be interested inmedical advances related to a particular affliction or disease. As aresult, the user 1802 interacts 1822 with a CILS to provide inputrelated to their various needs to a ranked insight model 1808. In turn,the factor-needs algorithm 1818 processes the user 1802 input todetermine the most relevant temporal topic model 1806, which is thenprovided to the user 1802.

The user 1802 then navigates the temporal topic model 1806, as describedin greater detail herein, to gain access to various temporal topics. Invarious embodiments, the user's 1802 interaction 1822 with the temporaltopic model 1806 is provided to the ranked insight model 1808, where itis used as training data. In certain embodiments, the training data isin turn processed by the factor-needs algorithm 1818 to generate a listof ranked insights 1820 related to the user's 1802 interest in aparticular affliction or disease, which is then provided to the user1802.

The process continues, with the user 1802 interacting 1822 with theranked insight model 1808, the hierarchical topic model 1804, and thetemporal topic model 1806 to iteratively receive a list of rankedinsights 1820. In various embodiments, the process is iterativelyrepeated to achieve continuous learning through feedback onobservations, decisions and results 1810, as described in greater detailherein. Skilled practitioners of the art will recognize that many suchembodiments and examples are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

FIGS. 19 a through 19 c are a generalized flowchart of continuouscognitive machine learning operations performed in accordance with anembodiment of the invention. In this embodiment, continuous cognitivemachine learning operations are begun in step 1902, followed by ongoingcorpora ingestion and curation operations, described in greater detailherein, being performed in steps 1904 and 1906. A user query or otherinput related to a user is then received in step 1908, followed by adetermination being made in step 1910 whether a relevant cognitivepersona or cognitive profile, described in greater detail herein, isavailable for the user. If so, then they are retrieved in step 1912 andthen processed in step 1914 for use as additional user input.

Thereafter, or if it was determined in step 1910 that a relevantcognitive persona or cognitive profile was not available, the user inputis then processed in step 1916 to identify relevant hierarchical andtemporal topic models. A determination is then made in step 1918 whethera relevant temporal topic model is available. If so, then user input andthe temporal topic model is processed in step 1920 with a factor-needsalgorithm to determine relevant events and their associated eventtopics.

A determination is then made in step 1922 whether a relevanthierarchical topic model is available. If not, then the relevant eventtopics determined in step 1920 are processed by the factor-needsalgorithm in step 1924 to generate a ranked list of event topics. Adetermination is then made in step 1926 whether both hierarchical topicand temporal topic models are available. If not, then the ranked list ofevent topics is provided to the user in step 1928 as a rankedrecommendation or cognitive insight. Otherwise, the user input, theranked list of event topics, and a relevant hierarchical topic model areprocessed in step 1930 with a factor-needs algorithm to determine thehighest relevant level of abstraction, and its associated domain topics,within the hierarchical topic model.

However, if it was respectively determined in steps 1918 and 1922 that arelevant temporal topic model was not available, or that a relevanthierarchical topic model was, then user input and the hierarchical topicmodel is processed in step 1932 with the factor-needs algorithm todetermine the highest relevant abstraction level, and its associateddomain topics, within the hierarchical topic model. The resultinghighest relevant abstraction level determined in either step 1930 orstep 1932, and its associated domain topics, is then provided to theuser in step 1934. A determination is then made in step 1936 whether theuser has selected one or more of the domain topics provided in step1934. If so, then cognitive machine learning training data is generatedin step 1938 by processing the user's domain topic selection(s) with ahierarchical topic navigation algorithm.

The factor-needs algorithm is then used in step 1940 to process theresulting cognitive machine learning training data, user input, rankedevent topics, if generated in step 1924, and the hierarchical topicmodel, to determine the next lowest relevant level of abstraction, andits associated domain topics, within the hierarchical topic model. Adetermination is then made in step 1942 whether the lowest level ofabstraction within the hierarchical topic model has been reached. Ifnot, the next lowest level of abstraction within the hierarchical topicmodel, and its associated domain topics, is provided to the user in step1946. The process is then continued, proceeding with step 1936.

However, if it was determined in step 1942 that the lowest level ofabstraction in the hierarchical topic model has been reached, then thefactor-needs algorithm is used in step 1946 to rank the domain topicsassociated with the lowest level of abstraction within the hierarchicaltopic model. Thereafter, or if it was determined in step 1936 that theuser has not selected one or more domain topics, then the previouslyranked domain topics, or the domain topics originally provided in step1934, are provided to the user in step 1948 as a ranked recommendationor cognitive insight.

Thereafter, or after the ranked event topics are provided to the user asa ranked recommendation or cognitive insight in step 1928, adetermination is made in step 1950 whether a response to the rankedrecommendation or cognitive insight provided in steps 1928 or 1948 isreceived from the user. If so, then the process is continued, proceedingwith step 1908. Otherwise, a determination is made in step 1952 whetherto end continuous cognitive machine learning operations. If not, thenthe process is ended, proceeding with step 1908. Otherwise, continuouscognitive machine learning operations are ended in step 1954.

FIGS. 20 a and 20 b are a simplified process flow diagram showing thegeneration of cognitive insights by a Cognitive Inference and LearningSystem (CILS) implemented in accordance with an embodiment of theinvention. In various embodiments, insight agents use a cognitive graph,such as an application cognitive graph 2082, as their data source togenerate individual cognitive insights. As used herein, an applicationcognitive graph 2082 broadly refers to a cognitive graph that isassociated with a particular cognitive application 304. In variousembodiments, different cognitive applications 304 may interact withdifferent application cognitive graphs 2082 to generate individualcognitive insights for a user. In certain embodiments, the resultingindividual cognitive insights are then composed to generate a set ofcognitive insights, which in turn is provided to a user in the form of acognitive insight summary 2048.

In various embodiments, the orchestration of the selected insight agentsis performed by the cognitive insight/learning engine 330 shown in FIG.3 . In certain embodiments, a subset of insight agents is selected toprovide cognitive insights to satisfy a graph query 2044, a contextualsituation, or some combination thereof. For example, it may bedetermined that a particular subset of insight agents may be suited toprovide a cognitive insight related to a particular user of a particulardevice, at a particular location, at a particular time, for a particularpurpose. In certain embodiments, the insight agents are selected fororchestration as a result of receiving direct or indirect input data2042 from a user.

In various embodiments, the direct user input data 2042 may be a naturallanguage inquiry. In certain embodiments, the indirect user input data2042 may include the location of a user's device or the purpose forwhich it is being used. As an example, the Geographical PositioningSystem (GPS) coordinates of the location of a user's mobile device maybe received as indirect user input data 2042. In certain embodiments,the direct or indirect user input data 2042 may include personalinformation that can be used to identify the user. In variousembodiments, a cognitive identity management module 2084 is implementedto manage personal information associated with the user. In variousembodiments, the cognitive identity management module 2084 isimplemented to interact with one or more cognitive applications 304.Skilled practitioners of the art will recognize that many suchembodiments are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

In various embodiments, cognitive insight generation and associatedfeedback operations may be performed in various phases. In thisembodiment, these phases include a data lifecycle 2036 phase, a learning2038 phase, and an application/insight composition 2040 phase. In thedata lifecycle 2036 phase, an instantiation of a cognitive platform 2010sources social data 2012, public data 2014, licensed data 2016,proprietary data 2018, and transaction data 2019 from various sources asdescribed in greater detail herein. In various embodiments, an exampleof a cognitive platform 2010 instantiation is the cognitive platform 310shown in FIG. 3 . In this embodiment, the instantiation of a cognitiveplatform 2010 includes a source 2006 component, a process 2008component, a deliver 2010 component, a cleanse 2020 component, an enrich2022 component, a filter/transform 2024 component, and a repair/reject2026 component. Likewise, as shown in FIG. 20 b , the process 2008component includes a repository of models 2028, described in greaterdetail herein.

In various embodiments, the process 2008 component is implemented toperform various cognitive insight generation and other processingoperations described in greater detail herein. In these embodiments, theprocess 2008 component is implemented to interact with the source 2006component, which in turn is implemented to perform various data sourcingoperations described in greater detail herein. In various embodiments,the sourcing operations are performed by one or more sourcing agents, aslikewise described in greater detail herein. The resulting sourced datais then provided to the process 2008 component. In turn, the process2008 component is implemented to interact with the cleanse 2020component, which is implemented to perform various data cleansingoperations familiar to those of skill in the art. As an example, thecleanse 2020 component may perform data normalization or pruningoperations, likewise known to skilled practitioners of the art. Incertain embodiments, the cleanse 2020 component may be implemented tointeract with the repair/reject 2026 component, which in turn isimplemented to perform various data repair or data rejection operationsknown to those of skill in the art.

Once data cleansing, repair and rejection operations are completed, theprocess 2008 component is implemented to interact with the enrich 2022component, which is implemented in various embodiments to performvarious data enrichment operations described in greater detail herein.Once data enrichment operations have been completed, the process 2008component is likewise implemented to interact with the filter/transform2024 component, which in turn is implemented to perform data filteringand transformation operations described in greater detail herein. Invarious embodiments, the process 2008 component is implemented togenerate various models, described in greater detail herein, which arestored in the repository of models 2028.

The process 2008 component is likewise implemented in variousembodiments to use the sourced data to generate one or more cognitivegraphs, such as an application cognitive graph 2082 and the transactionsknowledge repository 2078, as likewise described in greater detailherein. In various embodiments, the process 2008 component isimplemented to gain an understanding of the data sourced from thesources of social data 2012, public data 2014, device data 2016,proprietary data 2018, and transaction data 2019, which assist in theautomated generation of the application cognitive graph 2082 and thetransactions knowledge repository 2078.

The process 2008 component is likewise implemented in variousembodiments to perform bridging 2046 operations, described in greaterdetail herein, to access the application cognitive graph 2082 and thetransactions knowledge repository 2078. In certain embodiments, thebridging 2046 operations are performed by bridging agents, likewisedescribed in greater detail herein. In certain embodiments, theapplication cognitive graph 2082 and the transactions knowledgerepository 2078 is accessed by the process 2008 component during thelearn 2036 phase of the cognitive insight generation operations.

In various embodiments, a cognitive application 304 is implemented toreceive input data associated with an individual user or a group ofusers. In these embodiments, the input data may be direct, such as auser query or mouse click, or indirect, such as the current time orGeographical Positioning System (GPS) data received from a mobile deviceassociated with a user. In various embodiments, the indirect input datamay include contextual data, described in greater detail herein. Once itis received, the input data 2042 is then submitted by the cognitiveapplication 304 to a graph query engine 326 during theapplication/insight composition 2040 phase. In various embodiments, aninferred learning style, described in greater detail herein, isimplemented by the CILS to perform cognitive learning operation. Incertain embodiments, the CILS is likewise implemented to interpret theresults of the cognitive learning operations such that they areconsumable by a recipient, and by extension, present them in a form thatthis actionable in act 2040 phase. In various embodiments, the act 2040phase is implemented to support an interaction, described in greaterdetail herein.

The submitted input data 2042 is then processed by the graph queryengine 326 to generate a graph query 2044, as described in greaterdetail herein. The graph query 2044 is then used to query theapplication cognitive graph 2082, which results in the generation of oneor more cognitive insights, likewise described in greater detail herein.In certain embodiments, the graph query 2044 uses knowledge elementsstored in the universal knowledge repository 2080 and the transactionsknowledge repository 2078 when querying the application cognitive graph2082 to generate the one or more cognitive insights.

In various embodiments, the graph query 2044 results in the selection ofa cognitive persona from a cognitive personas repository 2072 accordingto a set of contextual information associated with a user. As usedherein, a cognitive persona broadly refers to an archetype user modelthat represents a common set of attributes associated with ahypothesized group of users. In various embodiments, the common set ofattributes may be described through the use of demographic, geographic,psychographic, behavioristic, and other information. As an example, thedemographic information may include age brackets (e.g., 25 to 34 yearsold), gender, marital status (e.g., single, married, divorced, etc.),family size, income brackets, occupational classifications, educationalachievement, and so forth. Likewise, the geographic information mayinclude the cognitive persona's typical living and working locations(e.g., rural, semi-rural, suburban, urban, etc.) as well ascharacteristics associated with individual locations (e.g., parochial,cosmopolitan, population density, etc.).

The psychographic information may likewise include information relatedto social class (e.g., upper, middle, lower, etc.), lifestyle (e.g.,active, healthy, sedentary, reclusive, etc.), interests (e.g., music,art, sports, etc.), and activities (e.g., hobbies, travel, going tomovies or the theatre, etc.). Other psychographic information may berelated to opinions, attitudes (e.g., conservative, liberal, etc.),preferences, motivations (e.g., living sustainably, exploring newlocations, etc.), and personality characteristics (e.g., extroverted,introverted, etc.) Likewise, the behavioristic information may includeinformation related to knowledge and attitude towards variousmanufacturers or organizations and the products or services they mayprovide. In various embodiments, one or more cognitive personas may beassociated with a user. In certain embodiments, a cognitive persona isselected and then used by a CILS to generate one or more cognitiveinsights as described in greater detail herein. In these embodiments,the cognitive insights that are generated for a user as a result ofusing a first cognitive persona may be different than the cognitiveinsights that are generated as a result of using a second cognitivepersona.

In various embodiments, provision of the cognitive insights results inthe CILS receiving feedback 2062 data from various individual users andother sources, such as a cognitive application 304. In one embodiment,the feedback 2062 data is used to revise or modify the cognitivepersona. In another embodiment, the feedback 2062 data is used to createa new cognitive persona. In yet another embodiment, the feedback 2062data is used to create one or more associated cognitive personas, whichinherit a common set of attributes from a source cognitive persona. Inone embodiment, the feedback 2062 data is used to create a new cognitivepersona that combines attributes from two or more source cognitivepersonas. In another embodiment, the feedback 2062 data is used tocreate a cognitive profile based upon the cognitive persona.

As used herein, a cognitive profile refers to an instance of a cognitivepersona that references personal data associated with a user. In variousembodiments, the personal data may include the user's name, address,Social Security Number (SSN), age, gender, marital status, occupation,employer, income, education, skills, knowledge, interests, preferences,likes and dislikes, goals and plans, and so forth. In certainembodiments, the personal data may include data associated with theuser's interaction with a CILS and related cognitive insights that aregenerated and provided to the user. In various embodiments, the personaldata may be distributed. In certain of these embodiments, subsets of thedistributed personal data may be logically aggregated to generate one ormore cognitive profiles, each of which is associated with the user. Invarious embodiments, the user's interaction with a CILS may be providedto the CILS as feedback 2062 data. In certain embodiments, the graphquery 2044 results in the selection of a cognitive profile from thecognitive profiles repository 2074 according to identificationinformation associated with a user. Those of skill in the art willrealize that many such embodiments are possible. Accordingly, theforegoing is not intended to limit the spirit, scope or intent of theinvention.

In certain embodiments, the universal knowledge repository 2080 includesthe cognitive personas repository 2072. In various embodiments, acognitive profiles repository 2074 is included in the cognitive personasrepository 2072. In certain embodiments, the universal knowledgerepository 2080 may contain a repository of session graphs 2052. Invarious embodiments, the universal knowledge repository 2080 may containthe transactions knowledge repository 2078. In certain embodiments,individual personas in the cognitive personas repository 2072 areimplemented as cognitive graphs.

In various embodiments, individual nodes within the cognitive personasstored in the cognitive personas repository 2072 are linked 2054 tocorresponding nodes in the universal knowledge repository 2080. Incertain embodiments, individual nodes within cognitive personas storedin the cognitive personas repository 2072 are linked 2054 tocorresponding nodes in the cognitive profiles repository 2074. Invarious embodiments, individual nodes within the cognitive personasrepository 2072, and individual nodes within the cognitive profiles2074, are linked 2054 to corresponding nodes in the transactionsknowledge repository 2078. In certain embodiments, individual nodeswithin the cognitive profiles repository 2074 are linked 2054 tocorresponding nodes within the universal knowledge repository 2080,which are likewise linked 2054 to corresponding nodes within thecognitive application graph 2082.

As used herein, contextual information broadly refers to informationassociated with a location, a point in time, a user role, an activity, acircumstance, an interest, a desire, a perception, an objective, or acombination thereof. In various embodiments, the contextual informationis likewise used in combination with the selected cognitive persona togenerate one or more cognitive insights for a user. In certainembodiments, the contextual information may likewise be used incombination with the selected cognitive persona to perform one or moreassociated cognitive learning operations. In various embodiments, thecognitive insights that are generated for a user as a result of using afirst set of contextual information may be different than the cognitiveinsights that are generated as a result of using a second set ofcontextual information.

In one embodiment, the result of using a first set of contextualinformation in combination with the selected cognitive persona toperform an associated cognitive learning operation may be different thanthe result of using a second set of contextual information incombination with the selected cognitive persona to perform the samecognitive learning operation. In another embodiment, the cognitiveinsights that are generated for a user as a result of using a set ofcontextual information with a first cognitive persona may be differentthan the cognitive insights that are generated as a result of using thesame set of contextual information with a second cognitive persona. Inyet another embodiment, the result of using a set of contextualinformation in combination with a first cognitive persona to perform anassociated cognitive learning operation may be different than the resultof using the same set of contextual information in combination with asecond cognitive persona to perform the same cognitive learningoperation.

As an example, a user may have two associated cognitive personas,“purchasing agent” and “retail shopper,” which are respectively selectedaccording to two sets of contextual information. In this example, the“purchasing agent” cognitive persona may be selected according to afirst set of contextual information associated with the user performingbusiness purchasing activities in their office during business hours,with the objective of finding the best price for a particular commercialinventory item. Conversely, the “retail shopper” cognitive persona maybe selected according to a second set of contextual informationassociated with the user performing cognitive personal shoppingactivities in their home over a weekend, with the objective of finding adecorative item that most closely matches their current furnishings.

Those of skill in the art will realize that the cognitive insightsgenerated as a result of combining the first cognitive persona with thefirst set of contextual information will likely be different than thecognitive insights generated as a result of combining the secondcognitive persona with the second set of contextual information.Likewise, the result of a cognitive learning operation that uses thefirst cognitive persona in combination with the first set of contextualinformation will likely be different that the result of a cognitivelearning operation that uses a second cognitive persona in combinationwith a second set of contextual information. Skilled practitioners ofthe art will recognize that many such embodiments are possible.Accordingly, the foregoing is not intended to limit the spirit, scope orintent of the invention.

In various embodiments, provision of the cognitive insights results inthe CILS receiving feedback 2062 information related to an individualuser. In one embodiment, the feedback 2062 information is used to reviseor modify a particular cognitive persona. In another embodiment, thefeedback 2062 information is used to revise or modify a cognitiveprofile associated with a user. In yet another embodiment, the feedback2062 information is used to create a new cognitive profile, which inturn is stored in the cognitive profiles repository 2074. In still yetanother embodiment, the feedback 2062 information is used to create oneor more associated cognitive profiles, which inherit a common set ofattributes from a source cognitive profile. In another embodiment, thefeedback 2062 information is used to create a new cognitive profile thatcombines attributes from two or more source cognitive profiles. Invarious embodiments, these persona and profile management operations2076 are performed through interactions between the cognitiveapplication 304, the cognitive identity management module 2084, thecognitive personas repository 2072, the cognitive profiles repository2074, the transactions knowledge repository 2078, the cognitive sessiongraphs repository 2052, the universal knowledge repository 2080, or somecombination thereof.

In various embodiments, the feedback 2062 is generated as a result of aninteraction. In various embodiments, the interaction may be between anycombination of devices, applications, services, processes, or users. Incertain embodiments, the interaction may be explicitly or implicitlyinitiated by the provision of input data 2042 to the devices,applications, services, processes or users. In various embodiments, theinput data 2042 may be provided in response to a cognitive insightprovided by a CILS. In one embodiment, the input data 2042 may include auser gesture, such as a key stroke, mouse click, finger swipe, or eyemovement. In another embodiment, the input data may include a voicecommand from a user.

In yet another embodiment, the input data 2042 may include dataassociated with a user, such as biometric data (e.g., retina scan,fingerprint, body temperature, pulse rate, etc.). In yet still anotherembodiment, the input data may include environmental data (e.g., currenttemperature, etc.), location data (e.g., geographical positioning systemcoordinates, etc.), device data (e.g., telemetry data, etc.), or otherdata provided by a device, application, service, process or user. Inthese embodiments, the feedback 2062 may be used to perform variouscognitive learning operations, the results of which are used to update acognitive persona or profile associated with a user. Those of skill inthe art will realize that many such embodiments are possible.Accordingly, the foregoing is not intended to limit the spirit, scope orintent of the invention.

In various embodiments, a cognitive profile associated with a user maybe either static or dynamic. As used herein, a static cognitive profilerefers to a cognitive profile that contains identification informationassociated with a user that changes on an infrequent basis. As anexample, a user's name, Social Security Number (SSN), or passport numbermay not change, although their age, address or employer may change overtime. To continue the example, the user may likewise have a variety offinancial account identifiers and various travel awards programidentifiers which change infrequently.

As likewise used herein, a dynamic cognitive profile refers to acognitive profile that contains information associated with a user thatchanges on a dynamic basis. For example, a user's interests andactivities may evolve over time, which may be evidenced by associatedinteractions 2050 with the CILS. In various embodiments, theseinteractions 2050 result in the provision of various cognitive insightsto the user. In certain embodiments, these interactions 2050 maylikewise be used to perform one or more associated cognitive learningoperations, the results of which may in turn be used to generate acognitive insight. In these embodiments, the user's interactions 2050with the CILS, and the resulting cognitive insights that are generated,are used to update the dynamic cognitive profile on an ongoing basis toprovide an up-to-date representation of the user in the context of thecognitive profile used to generate the cognitive insights.

In various embodiments, a cognitive profile, whether static or dynamic,is selected from the cognitive profiles repository 2074 according to aset of contextual information associated with a user. In certainembodiments, the contextual information is likewise used in combinationwith the selected cognitive profile to generate one or more cognitiveinsights for the user. In various embodiments, the contextualinformation may likewise be used in combination with the selectedcognitive profile to perform one or more associated cognitive learningoperations. In one embodiment, the cognitive insights that are generatedas a result of using a first set of contextual information incombination with the selected cognitive profile may be different thanthe cognitive insights that are generated as a result of using a secondset of contextual information with the same cognitive profile. Inanother embodiment, the result of using a first set of contextualinformation in combination with the selected cognitive profile toperform an associated cognitive learning operation may be different thanthe result of using a second set of contextual information incombination with the selected cognitive profile to perform the samecognitive learning operation.

In various embodiments, one or more cognitive profiles may be associatedwith a user. In certain embodiments, the cognitive insights that aregenerated for a user as a result of using a set of contextualinformation with a first cognitive profile may be different than thecognitive insights that are generated as a result of using the same setof contextual information with a second cognitive profile. In oneembodiment, the result of using a set of contextual information incombination with a first cognitive profile to perform an associatedcognitive learning operation may be different than the result of usingthe same set of contextual information in combination with a secondcognitive profile to perform the same cognitive learning operation.

As an example, a user may have two associated cognitive profiles,“runner” and “foodie,” which are respectively selected according to twosets of contextual information. In this example, the “runner” cognitiveprofile may be selected according to a first set of contextualinformation associated with the user being out of town on businesstravel and wanting to find a convenient place to run close to where theyare staying. To continue this example, the contextual information may bebooking and payment information contained within a blockchaintransaction associated with the user. To further continue this example,two cognitive insights may be generated and provided to the user in theform of a cognitive insight summary 2048. The first may be suggesting arunning trail the user has used before and liked, but needs directionsto find again. The second may be suggesting a new running trail that isequally convenient, but wasn't available the last time the user was intown.

Conversely, the “foodie” cognitive profile may be selected according toa second set of contextual information associated with the user being athome and expressing an interest in trying either a new restaurant or aninnovative cuisine. In furtherance of this example, the user's “foodie”cognitive profile may be processed by the CILS to determine whichrestaurants and cuisines the user has tried in the last eighteen months.In this example, the contextual information may be ordering and paymentinformation contained in various blockchain transactions associated withthe user. As a result, two cognitive insights may be generated andprovided to the user in the form of a cognitive insight summary 2048.The first may be a suggestion for a new restaurant that is serving acuisine the user has enjoyed in the past. The second may be a suggestionfor a restaurant familiar to the user that includes a promotional offerfor a seasonal menu featuring Asian fusion dishes the user has not triedbefore.

Those of skill in the art will realize that the cognitive insightsgenerated as a result of combining the first cognitive profile with thefirst set of contextual information will likely be different than thecognitive insights generated as a result of combining the secondcognitive profile with the second set of contextual information.Likewise, the result of a cognitive learning operation that uses thefirst cognitive profile in combination with the first set of contextualinformation will likely be different that the result of a cognitivelearning operation that uses a second cognitive profile in combinationwith a second set of contextual information.

In various embodiments, a user's cognitive profile, whether static ordynamic, may reference data that is proprietary to the user, a group, anorganization, or some combination thereof. As used herein, proprietarydata broadly refers to data that is owned, controlled, or a combinationthereof, by an individual user, group, or organization, which is deemedimportant enough that it gives competitive advantage to that individualor organization. In certain embodiments, the organization may be agovernmental, non-profit, academic or social entity, a manufacturer, awholesaler, a retailer, a service provider, an operator of a cognitiveinference and learning system (CILS), and others.

In various embodiments, an organization may or may not grant a user theright to obtain a copy of certain proprietary information referenced bytheir cognitive profile. In certain embodiments, access to theproprietary information may be controlled through the implementation ofa cognitive identity management module 2084. In various embodiments, afirst organization may or may not grant a user the right to obtain acopy of certain proprietary information referenced by their cognitiveprofile and provide it to a second organization. As an example, the usermay not be granted the right to provide travel detail information (e.g.,travel dates and destinations, etc.) associated with an awards programprovided by a first travel services provider (e.g., an airline, a hotelchain, a cruise ship line, etc.) to a second travel services provider.In various embodiments, the user may or may not grant a firstorganization the right to provide a copy of certain proprietaryinformation referenced by their cognitive profile to a secondorganization. Those of skill in the art will recognize that many suchembodiments are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

In various embodiments, a set of contextually-related interactionsbetween a cognitive application 304 and the application cognitive graph2082 are represented as a corresponding set of nodes in a cognitivesession graph, which is then stored in a cognitive session graphsrepository 2052. As used herein, a cognitive session graph broadlyrefers to a cognitive graph whose nodes are associated with a cognitivesession. As used herein, a cognitive session broadly refers to a user,group of users, theme, topic, issue, question, intent, goal, objective,task, assignment, process, situation, requirement, condition,responsibility, location, period of time, a block in a blockchain, ablockchain transaction associated with a blockchain block, or anycombination thereof. In various embodiments, the results of a cognitivelearning operation, described in greater detail herein, may be stored ina session graph.

In certain embodiments, a cognitive session graph is used to generate acognitive insight for a user. As an example, the application cognitivegraph 2082 may be unaware of a particular user's preferences, which arelikely stored in a corresponding user profile. To further the example, auser may typically choose a particular brand or manufacturer whenshopping for a given type of product, such as cookware, therebyindicating their preferences. A record of each query regarding thatbrand of cookware, or its selection, is iteratively stored in a sessiongraph that is associated with the user and stored in a repository ofsession graphs 2052. As a result, the preference of that brand ofcookware is ranked higher, and a cognitive insight containingpromotional offer for that brand of cookware is presented in response tothe contextually-related queries, even when the preferred brand ofcookware is not explicitly referenced by the user. To continue theexample, the user may make a number of queries over a period of days orweeks. However, the queries, and their corresponding cognitive insights,are associated with the same cognitive session graph that is associatedwith the user. Furthermore, the queries and their correspondingcognitive insights are respectively stored in the repository of sessiongraphs 2052 and transactions knowledge repository 2078, regardless ofwhen each query is made. In this example, the record of each query, andtheir corresponding cognitive insight, is used to perform an associatedcognitive learning operation, the results of which may be stored in anassociated session graph.

As another example, a user may submit a query to a cognitive application304 during business hours to find an upscale restaurant located closetheir place of business. As a result, a first cognitive session graphstored in a cognitive session graphs repository 2052 is associated withthe user's query, which results in the provision of cognitive insightsrelated to restaurants suitable for business meetings. To continue theexample, the same user queries the same cognitive application 304 duringthe weekend to locate a casual restaurant located close to their home.As a result, a second cognitive session graph stored in a cognitivesession graphs repository 2052 is associated with the user's query,which results in the provision of cognitive insights related torestaurants suitable for family meals. In these examples, the first andsecond cognitive session graphs are both associated with the same user,but for two different purposes, which results in the provision of twodifferent sets of cognitive insights.

As yet another example, a group of customer support representatives istasked with resolving technical issues customers may have with aproduct. In this example, the product and the group of customer supportrepresentatives are collectively associated with a cognitive sessiongraph stored in a cognitive session graphs repository 2052. To continuethe example, individual customer support representatives may submitqueries related to the product to a cognitive application 304, such as aknowledge base application. In response, a cognitive session graphstored in a cognitive session graphs repository 2052 is used, along withtransactions knowledge repository 2078, the universal knowledgerepository 2080, and application cognitive graph 2082, to generateindividual or composite cognitive insights to resolve a technical issuefor a customer. In this example, the cognitive application 304 may bequeried by the individual customer support representatives at differenttimes during some time interval, yet the same cognitive session graphstored in a cognitive session graphs repository 2052 is used to generatecognitive insights related to the product.

In various embodiments, each cognitive session graph associated with auser, and stored in a cognitive session graphs repository 2052, includesone or more direct or indirect user queries represented as nodes, andthe time at which they were asked, which are in turn linked 2054 tonodes that appear in the application cognitive graph 2082. In certainembodiments, each individual cognitive session graph that is associatedwith the user and stored in a cognitive session graphs repository 2052introduces edges that are not already present in the applicationcognitive graph 2082. More specifically, each of the cognitive sessiongraphs that is associated with the user and stored in a cognitivesession graphs repository 2052 establishes various relationships thatthe application cognitive graph 2082 does not already have.

In various embodiments, individual cognitive profiles in the cognitiveprofiles repository 2074 are respectively stored as session graphs inthe repository of session graphs 2052. In these embodiments, nodeswithin each of the individual cognitive profiles are linked 2054 tonodes within corresponding cognitive session graphs stored in therepository of cognitive session graphs 1″ through ‘n’ 2054. In certainembodiments, individual nodes within each of the cognitive profiles arelikewise linked 2054 to corresponding nodes within various cognitivepersonas stored in the cognitive personas repository 2072.

In various embodiments, individual graph queries 2044 associated with asession graph stored in a cognitive session graphs repository 2052 arelikewise provided to insight agents to perform various kinds ofanalyses. In certain embodiments, each insight agent performs adifferent kind of analysis. In various embodiments, different insightagents may perform the same, or similar, analyses. In certainembodiments, different agents performing the same or similar analysesmay be competing between themselves.

For example, a user may be a realtor that has a young, uppermiddle-class, urban-oriented clientele that typically enjoys eating attrendy restaurants that are in walking distance of where they live. As aresult, the realtor may be interested in knowing about new or popularrestaurants that are in walking distance of their property listings thathave a young, middle-class clientele. In this example, the user'squeries may result the assignment of insight agents to perform analysisof various social media interactions to identify such restaurants thathave received favorable reviews. To continue the example, the resultingcognitive insights may be provided as a ranked list of candidaterestaurants that may be suitable venues for the realtor to meet hisclients.

In various embodiments, the process 2008 component is implemented toprovide these cognitive insights to the deliver 2010 component, which inturn is implemented to deliver the cognitive insights in the form of acognitive insight summary 2048 to the cognitive business processes andapplications 304. In these embodiments, the cognitive platform 2010 isimplemented to interact with an insight front-end 2056 component, whichprovides a composite insight and feedback interface with the cognitiveapplication 304. In certain embodiments, the insight front-end 2056component includes an insight Application Program Interface (API) 2058and a feedback API 2060, described in greater detail herein. In theseembodiments, the insight API 2058 is implemented to convey the cognitiveinsight summary 2048 to the cognitive application 304. Likewise, thefeedback API 2060 is used to convey associated direct or indirect userfeedback 2062 to the cognitive platform 2010. In certain embodiments,the feedback API 2060 provides the direct or indirect user feedback 2062to the repository of models 2028 described in greater detail herein.

To continue the preceding example, the user may have received a list ofcandidate restaurants that may be suitable venues for meeting hisclients. However, one of his clients has a pet that they like to takewith them wherever they go. As a result, the user provides feedback 2062that he is looking for a restaurant that is pet-friendly. The providedfeedback 2062 is in turn provided to the insight agents to identifycandidate restaurants that are also pet-friendly. In this example, thefeedback 2062 is stored in the appropriate cognitive session graph 2052associated with the user and their original query.

In various embodiments, as described in the descriptive text associatedwith FIGS. 4, 6, 7 a and 7 b, cognitive learning operations areiteratively performed during the learn 2036 phase to provide moreaccurate and useful cognitive insights. In certain of these embodiments,feedback 2062 received from the user is stored in a session graph thatis associated with the user and stored in a repository of session graphs2052, which is then used to provide more accurate cognitive insights inresponse to subsequent contextually-relevant queries from the user. Invarious embodiments, the feedback 2062 received from the user is used toperform cognitive learning operations, the results of which are thenstored in a session graph that is associated with the user. In theseembodiments, the session graph associated with the user is stored in arepository of session graphs 2052.

As an example, cognitive insights provided by a particular insight agentrelated to a first subject may not be relevant or particularly useful toa user of a cognitive application 304. As a result, the user providesfeedback 2062 to that effect, which in turn is stored in the appropriatesession graph that is associated with the user and stored in arepository of session graphs 2052. Accordingly, subsequent insightsprovided by the insight agent related the first subject may be rankedlower, or not provided, within a cognitive insight summary 2048 providedto the user. Conversely, the same insight agent may provide excellentcognitive insights related to a second subject, resulting in positivefeedback 2062 being received from the user. The positive feedback 2062is likewise stored in the appropriate session graph that is associatedwith the user and stored in a repository of session graphs 2052. As aresult, subsequent cognitive insights provided by the insight agentrelated to the second subject may be ranked higher within a cognitiveinsight summary 2048 provided to the user.

In various embodiments, the cognitive insights provided in eachcognitive insight summary 2048 to the cognitive application 304, andcorresponding feedback 2062 received from a user in return, is providedto an associated session graph 2052 in the form of one or more insightstreams 2064. In these and other embodiments, the insight streams 2064may contain information related to the user of the cognitive application304, the time and date of the provided cognitive insights and relatedfeedback 2062, the location of the user, and the device used by theuser.

As an example, a query related to upcoming activities that is receivedat 10:00 AM on a Saturday morning from a user's home may returncognitive insights related to entertainment performances scheduled forthe weekend. Conversely, the same query received at the same time on aMonday morning from a user's office may return cognitive insightsrelated to business functions scheduled during the work week. In variousembodiments, the information contained in the insight streams 2064 isused to rank the cognitive insights provided in the cognitive insightsummary 2048. In certain embodiments, the cognitive insights arecontinually re-ranked as additional insight streams 2064 are received.Skilled practitioners of the art will recognize that many suchembodiments are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

Although the present invention has been described in detail, it shouldbe understood that various changes, substitutions and alterations can bemade hereto without departing from the spirit and scope of the inventionas defined by the appended claims.

What is claimed is:
 1. A computer-implementable method for generating acognitive insight comprising: receiving data, the data comprising aplurality of examples, each of the plurality of examples comprising aninput object and a desired output value, at least some of the pluralityof examples being based upon feedback from a user; performing a machinelearning operation on the data, the machine learning operationcomprising performing an augmented gamma belief network operation, theaugmented gamma belief network operation producing an inferred functionbased upon the data; performing a cognitive learning operation via acognitive inference and learning system using the plurality of examples,the cognitive learning operation implementing a plurality of cognitivelearning techniques according to a cognitive learning framework, thecognitive learning operation applying the plurality of cognitivelearning techniques via the machine learning operation to generate acognitive learning result; generating a cognitive insight based upon acognitive profile generated using the inferred function generated by theaugmented gamma belief network operation; and wherein the plurality ofcognitive learning techniques comprise a direct correlations cognitivelearning technique, an explicit likes/dislikes cognitive learningtechnique, a patterns and concepts cognitive learning technique, abehavior cognitive learning technique, a concept entailment cognitivelearning technique, and a contextual recommendation cognitive learningtechnique, the direct correlations cognitive learning technique beingassociated with a declared learning style and bounded by a data-basedcognitive learning category, the explicit likes/dislikes cognitivelearning technique being associated with the declared learning style andbounded by an interaction-based cognitive learning category, thepatterns and concepts cognitive learning technique being associated withan observed learning style and bounded by the data-based cognitivelearning category, the behavior cognitive learning technique beingassociated with the observed learning style and bounded by theinteraction-based cognitive learning category, the concept entailmentcognitive learning technique being associated with an inferred learningstyle and bounded by the data-based cognitive learning category, and acontextual recommendation cognitive learning technique being associatedwith the inferred learning style and bounded by the interaction-basedcognitive learning category.
 2. The method of claim 1, wherein: theaugmented gamma belief network operation factorizes each of a pluralityof hidden layers into a product of a space connection weight matrix. 3.The method of claim 1, wherein: the augmented gamma belief networkoperation factorizes each of a plurality of hidden layers nonnegativereal hidden units of a next layer of abstraction.
 4. The method of claim1, wherein: each of a plurality of hidden layers are trained via a Gibbssampler operation, the Gibb sampler operation performing an upwardsampling operation and a downward sampling operation on each of theplurality of hidden layers.
 5. The method of claim 4, wherein: eachupward sampling operation propagates latent counts and samples Dirichletdistributed connection weight vectors starting from a bottom-most layerof the plurality of hidden layers.
 6. The method of claim 4, wherein:each downward sampling operation samples gamma distributed hidden unitsstarting from a top hidden layer with each next lower hidden layersolved with a same subroutine.
 7. A system comprising: a processor; adata bus coupled to the processor; and a non-transitory,computer-readable storage medium embodying computer program code, thenon-transitory, computer-readable storage medium being coupled to thedata bus, the computer program code interacting with a plurality ofcomputer operations and comprising instructions executable by theprocessor and configured for: receiving data, the data comprising aplurality of examples, each of the plurality of examples comprising aninput object and a desired output value, at least some of the pluralityof examples being based upon feedback from a user; performing a machinelearning operation on the data, the machine learning operationcomprising performing an augmented gamma belief network operation, theaugmented gamma belief network operation producing an inferred functionbased upon the data; performing a cognitive learning operation via acognitive inference and learning system using the plurality of examples,the cognitive learning operation implementing a plurality of cognitivelearning techniques according to a cognitive learning framework, thecognitive learning operation applying the plurality of cognitivelearning techniques via the machine learning operation to generate acognitive learning result; generating a cognitive insight based upon acognitive profile generated using the inferred function generated by theaugmented gamma belief network operation; and wherein the plurality ofcognitive learning techniques comprise a direct correlations cognitivelearning technique, an explicit likes/dislikes cognitive learningtechnique, a patterns and concepts cognitive learning technique, abehavior cognitive learning technique, a concept entailment cognitivelearning technique, and a contextual recommendation cognitive learningtechnique, the direct correlations cognitive learning technique beingassociated with a declared learning style and bounded by a data-basedcognitive learning category, the explicit likes/dislikes cognitivelearning technique being associated with the declared learning style andbounded by an interaction-based cognitive learning category, thepatterns and concepts cognitive learning technique being associated withan observed learning style and bounded by the data-based cognitivelearning category, the behavior cognitive learning technique beingassociated with the observed learning style and bounded by theinteraction-based cognitive learning category, the concept entailmentcognitive learning technique being associated with an inferred learningstyle and bounded by the data-based cognitive learning category, and acontextual recommendation cognitive learning technique being associatedwith the inferred learning style and bounded by the interaction-basedcognitive learning category.
 8. The system of claim 7, wherein: theaugmented gamma belief network operation factorizes each of a pluralityof hidden layers into a product of a space connection weight matrix. 9.The system of claim 7, wherein: the augmented gamma belief networkoperation factorizes each of a plurality of hidden layers nonnegativereal hidden units of a next layer of abstraction.
 10. The system ofclaim 7, wherein: each of a plurality of hidden layers are trained via aGibbs sampler operation, the Gibb sampler operation performing an upwardsampling operation and a downward sampling operation on each of theplurality of hidden layers.
 11. The system of claim 10, wherein: eachupward sampling operation propagates latent counts and samples Dirichletdistributed connection weight vectors starting from a bottom-most layerof the plurality of hidden layers.
 12. The system of claim 10, wherein:each downward sampling operation samples gamma distributed hidden unitsstarting from a top hidden layer with each next lower hidden layersolved with a same subroutine.
 13. A non-transitory, computer-readablestorage medium embodying computer program code, the computer programcode comprising computer executable instructions configured for:receiving data, the data comprising a plurality of examples, each of theplurality of examples comprising an input object and a desired outputvalue, at least some of the plurality of examples being based uponfeedback from a user; performing a machine learning operation on thedata, the machine learning operation comprising performing an augmentedgamma belief network operation, the augmented gamma belief networkoperation producing an inferred function based upon the data; performinga cognitive learning operation via a cognitive inference and learningsystem using the plurality of examples, the cognitive learning operationimplementing a plurality of cognitive learning techniques according to acognitive learning framework, the cognitive learning operation applyingthe plurality of cognitive learning techniques via the machine learningoperation to generate a cognitive learning result; generating acognitive insight based upon a cognitive profile generated using theinferred function generated by the augmented gamma belief networkoperation; and wherein the plurality of cognitive learning techniquescomprising a direct correlations cognitive learning technique, anexplicit likes/dislikes cognitive learning technique, a patterns andconcepts cognitive learning technique, a behavior cognitive learningtechnique, a concept entailment cognitive learning technique, and acontextual recommendation cognitive learning technique, the directcorrelations cognitive learning technique being associated with adeclared learning style and bounded by a data-based cognitive learningcategory, the explicit likes/dislikes cognitive learning technique beingassociated with the declared learning style and bounded by aninteraction-based cognitive learning category, the patterns and conceptscognitive learning technique being associated with an observed learningstyle and bounded by the data-based cognitive learning category, thebehavior cognitive learning technique being associated with the observedlearning style and bounded by the interaction-based cognitive learningcategory, the concept entailment cognitive learning technique beingassociated with an inferred learning style and bounded by the data-basedcognitive learning category, and a contextual recommendation cognitivelearning technique being associated with the inferred learning style andbounded by the interaction-based cognitive learning category.
 14. Thenon-transitory, computer-readable storage medium of claim 13, wherein:the augmented gamma belief network operation factorizes each of aplurality of hidden layers into a product of a space connection weightmatrix.
 15. The non-transitory, computer-readable storage medium ofclaim 13, wherein: the augmented gamma belief network operationfactorizes each of a plurality of hidden layers nonnegative real hiddenunits of a next layer of abstraction.
 16. The non-transitory,computer-readable storage medium of claim 13, wherein: each of aplurality of hidden layers are trained via a Gibbs sampler operation,the Gibb sampler operation performing an upward sampling operation and adownward sampling operation on each of the plurality of hidden layers.17. The non-transitory, computer-readable storage medium of claim 16,wherein: each upward sampling operation propagates latent counts andsamples Dirichlet distributed connection weight vectors starting from abottom-most layer of the plurality of hidden layers.
 18. Thenon-transitory, computer-readable storage medium of claim 16, wherein:each downward sampling operation samples gamma distributed hidden unitsstarting from a top hidden layer with each next lower hidden layersolved with a same subroutine.
 19. The non-transitory, computer-readablestorage medium of claim 13, wherein the computer executable instructionsare deployable to a client system from a server system at a remotelocation.
 20. The non-transitory, computer-readable storage medium ofclaim 13, wherein the computer executable instructions are provided by aservice provider to a user on an on-demand basis.